GAMPCA
Edgar Espejo
2022-09-02
Comparación de fotos de los tipos de fracturas
# libreria de lectura de bitmaps para bmp, jpeg, png y tiff
library(readbitmap)## Warning: package 'readbitmap' was built under R version 4.1.2library(fda)## Warning: package 'fda' was built under R version 4.1.3## Loading required package: splines## Loading required package: fds## Warning: package 'fds' was built under R version 4.1.3## Loading required package: rainbow## Warning: package 'rainbow' was built under R version 4.1.3## Loading required package: MASS## Warning: package 'MASS' was built under R version 4.1.3## Loading required package: pcaPP## Warning: package 'pcaPP' was built under R version 4.1.3## Loading required package: RCurl## Warning: package 'RCurl' was built under R version 4.1.3## Loading required package: deSolve## Warning: package 'deSolve' was built under R version 4.1.3##
## Attaching package: 'fda'## The following object is masked from 'package:graphics':
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## matplotlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.1.3library(dplyr)## Warning: package 'dplyr' was built under R version 4.1.3##
## Attaching package: 'dplyr'## The following object is masked from 'package:MASS':
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## select## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(caret)## Warning: package 'caret' was built under R version 4.1.3## Loading required package: lattice## Warning: package 'lattice' was built under R version 4.1.3##
## Attaching package: 'lattice'## The following object is masked from 'package:fda':
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## melanomalibrary(fda.usc)## Warning: package 'fda.usc' was built under R version 4.1.3## Loading required package: mgcv## Warning: package 'mgcv' was built under R version 4.1.3## Loading required package: nlme## Warning: package 'nlme' was built under R version 4.1.3##
## Attaching package: 'nlme'## The following object is masked from 'package:dplyr':
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## collapse## This is mgcv 1.8-41. For overview type 'help("mgcv-package")'.## fda.usc is running sequentially usign foreach package
## Please, execute ops.fda.usc() once to run in local parallel mode
## Deprecated functions: min.basis, min.np, anova.hetero, anova.onefactor, anova.RPm
## New functions: optim.basis, optim.np, fanova.hetero, fanova.onefactor, fanova.RPm
## ----------------------------------------------------------------------------------1. Cargar imagenes dúctiles
Total de imagenes 10. Por cada foto 400 datos funcionales (lineas de pixeles en X y líneas de pixeles en y), para un total de 4000 datos.
imagenductilent1 <- read.bitmap("DuctilEnt1.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent1 <- imagenductilent1 [,,1] # Deja un solo canal
imagenductilent1t <- t(imagenductilent1)
imagenductilent1xey <- rbind(imagenductilent1,imagenductilent1t)
imagenductilent1xey <- t(imagenductilent1xey)
dim(imagenductilent1xey)## [1] 200 400imagenductilent2 <- read.bitmap("DuctilEnt2.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent2 <- imagenductilent2 [,,1] # Deja un solo canal
imagenductilent2t <- t(imagenductilent2)
imagenductilent2xey <- rbind(imagenductilent2,imagenductilent2t)
imagenductilent2xey <- t(imagenductilent2xey)
dim(imagenductilent2xey)## [1] 200 400imagenductilent3 <- read.bitmap("DuctilEnt3.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent3 <- imagenductilent3 [,,1] # Deja un solo canal
imagenductilent3t <- t(imagenductilent3)
imagenductilent3xey <- rbind(imagenductilent3,imagenductilent3t)
imagenductilent3xey <- t(imagenductilent3xey)
dim(imagenductilent3xey)## [1] 200 400imagenductilent4 <- read.bitmap("DuctilEnt4.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent4 <- imagenductilent4 [,,1] # Deja un solo canal
imagenductilent4t <- t(imagenductilent4)
imagenductilent4xey <- rbind(imagenductilent4,imagenductilent4t)
imagenductilent4xey <- t(imagenductilent4xey)
dim(imagenductilent4xey)## [1] 200 400imagenductilent5 <- read.bitmap("DuctilEnt5.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent5 <- imagenductilent5 [,,1] # Deja un solo canal
imagenductilent5t <- t(imagenductilent5)
imagenductilent5xey <- rbind(imagenductilent5,imagenductilent5t)
imagenductilent5xey <- t(imagenductilent5xey)
dim(imagenductilent5xey)## [1] 200 400imagenductilent6 <- read.bitmap("DuctilEnt6.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent6 <- imagenductilent6 [,,1] # Deja un solo canal
imagenductilent6t <- t(imagenductilent6)
imagenductilent6xey <- rbind(imagenductilent6,imagenductilent6t)
imagenductilent6xey <- t(imagenductilent6xey)
dim(imagenductilent6xey)## [1] 200 400imagenductilent7 <- read.bitmap("DuctilEnt7.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent7 <- imagenductilent7 [,,1] # Deja un solo canal
imagenductilent7t <- t(imagenductilent7)
imagenductilent7xey <- rbind(imagenductilent7,imagenductilent7t)
imagenductilent7xey <- t(imagenductilent7xey)
dim(imagenductilent7xey)## [1] 200 400imagenductilens1 <- read.bitmap("DuctilEns1.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilens1 <- imagenductilens1 [,,1] # Deja un solo canal
imagenductilens1t <- t(imagenductilens1)
imagenductilens1xey <- rbind(imagenductilens1,imagenductilens1t)
imagenductilens1xey <- t(imagenductilens1xey)
dim(imagenductilens1xey)## [1] 200 400imagenductilens2 <- read.bitmap("DuctilEns2.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilens2 <- imagenductilens2 [,,1] # Deja un solo canal
imagenductilens2t <- t(imagenductilens2)
imagenductilens2xey <- rbind(imagenductilens2,imagenductilens2t)
imagenductilens2xey <- t(imagenductilens2xey)
dim(imagenductilens2xey)## [1] 200 400imagenductilens3 <- read.bitmap("DuctilEns3.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilens3 <- imagenductilens3 [,,1] # Deja un solo canal
imagenductilens3t <- t(imagenductilens3)
imagenductilens3xey <- rbind(imagenductilens3,imagenductilens3t)
imagenductilens3xey <- t(imagenductilens3xey)
dim(imagenductilens3xey)## [1] 200 400imagenductilxey <- cbind(imagenductilent1xey,imagenductilent2xey,imagenductilent3xey,imagenductilent4xey,imagenductilent5xey,imagenductilent6xey,imagenductilent7xey,imagenductilens1xey,imagenductilens2xey,imagenductilens3xey)
dim(imagenductilxey)## [1] 200 40002. Cargar imagenes frágiles
Total de imagenes 10. Por cada foto 400 datos funcionales (lineas de pixeles en X y líneas de pixeles en y), para un total de 4000 datos.
imagenfragilent1 <- read.bitmap("FragilEnt1.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent1 <- imagenfragilent1 [,,1] # Deja un solo canal
imagenfragilent1t <- t(imagenfragilent1)
imagenfragilent1xey <- rbind(imagenfragilent1,imagenfragilent1t)
imagenfragilent1xey <- t(imagenfragilent1xey)
dim(imagenfragilent1xey)## [1] 200 400imagenfragilent2 <- read.bitmap("FragilEnt2.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent2 <- imagenfragilent2 [,,1] # Deja un solo canal
imagenfragilent2t <- t(imagenfragilent2)
imagenfragilent2xey <- rbind(imagenfragilent2,imagenfragilent2t)
imagenfragilent2xey <- t(imagenfragilent2xey)
dim(imagenfragilent2xey)## [1] 200 400imagenfragilent3 <- read.bitmap("FragilEnt3.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent3 <- imagenfragilent3 [,,1] # Deja un solo canal
imagenfragilent3t <- t(imagenfragilent3)
imagenfragilent3xey <- rbind(imagenfragilent3,imagenfragilent3t)
imagenfragilent3xey <- t(imagenfragilent3xey)
dim(imagenfragilent3xey)## [1] 200 400imagenfragilent4 <- read.bitmap("FragilEnt4.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent4 <- imagenfragilent4 [,,1] # Deja un solo canal
imagenfragilent4t <- t(imagenfragilent4)
imagenfragilent4xey <- rbind(imagenfragilent4,imagenfragilent4t)
imagenfragilent4xey <- t(imagenfragilent4xey)
dim(imagenfragilent4xey)## [1] 200 400imagenfragilent5 <- read.bitmap("FragilEnt5.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent5 <- imagenfragilent5 [,,1] # Deja un solo canal
imagenfragilent5t <- t(imagenfragilent5)
imagenfragilent5xey <- rbind(imagenfragilent5,imagenfragilent5t)
imagenfragilent5xey <- t(imagenfragilent5xey)
dim(imagenfragilent5xey)## [1] 200 400imagenfragilent6 <- read.bitmap("FragilEnt6.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent6 <- imagenfragilent6 [,,1] # Deja un solo canal
imagenfragilent6t <- t(imagenfragilent6)
imagenfragilent6xey <- rbind(imagenfragilent6,imagenfragilent6t)
imagenfragilent6xey <- t(imagenfragilent6xey)
dim(imagenfragilent6xey)## [1] 200 400imagenfragilent7 <- read.bitmap("FragilEnt7.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent7 <- imagenfragilent7 [,,1] # Deja un solo canal
imagenfragilent7t <- t(imagenfragilent7)
imagenfragilent7xey <- rbind(imagenfragilent7,imagenfragilent7t)
imagenfragilent7xey <- t(imagenfragilent7xey)
dim(imagenfragilent7xey)## [1] 200 400imagenfragilens1 <- read.bitmap("FragilEns1.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilens1 <- imagenfragilens1 [,,1] # Deja un solo canal
imagenfragilens1t <- t(imagenfragilens1)
imagenfragilens1xey <- rbind(imagenfragilens1,imagenfragilens1t)
imagenfragilens1xey <- t(imagenfragilens1xey)
dim(imagenfragilens1xey)## [1] 200 400imagenfragilens2 <- read.bitmap("FragilEns2.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilens2 <- imagenfragilens2 [,,1] # Deja un solo canal
imagenfragilens2t <- t(imagenfragilens2)
imagenfragilens2xey <- rbind(imagenfragilens2,imagenfragilens2t)
imagenfragilens2xey <- t(imagenfragilens2xey)
dim(imagenfragilens2xey)## [1] 200 400imagenfragilens3 <- read.bitmap("fragilEns3.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilens3 <- imagenfragilens3 [,,1] # Deja un solo canal
imagenfragilens3t <- t(imagenfragilens3)
imagenfragilens3xey <- rbind(imagenfragilens3,imagenfragilens3t)
imagenfragilens3xey <- t(imagenfragilens3xey)
dim(imagenfragilens3xey)## [1] 200 400imagenfragilxey <- cbind(imagenfragilent1xey,imagenfragilent2xey,imagenfragilent3xey,imagenfragilent4xey,imagenfragilent5xey,imagenfragilent6xey,imagenfragilent7xey,imagenfragilens1xey,imagenfragilens2xey,imagenfragilens3xey)
dim(imagenfragilxey)## [1] 200 40003. Cargar imagenes fatiga
Total de imagenes 10. Por cada foto 400 datos funcionales (lineas de pixeles en X y líneas de pixeles en y), para un total de 4000 datos.
imagenfatigaent1 <- read.bitmap("FatigaEnt1.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent1 <- imagenfatigaent1 [,,1] # Deja un solo canal
imagenfatigaent1t <- t(imagenfatigaent1)
imagenfatigaent1xey <- rbind(imagenfatigaent1,imagenfatigaent1t)
imagenfatigaent1xey <- t(imagenfatigaent1xey)
dim(imagenfatigaent1xey)## [1] 200 400imagenfatigaent2 <- read.bitmap("FatigaEnt2.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent2 <- imagenfatigaent2 [,,1] # Deja un solo canal
imagenfatigaent2t <- t(imagenfatigaent2)
imagenfatigaent2xey <- rbind(imagenfatigaent2,imagenfatigaent2t)
imagenfatigaent2xey <- t(imagenfatigaent2xey)
dim(imagenfatigaent2xey)## [1] 200 400imagenfatigaent3 <- read.bitmap("FatigaEnt3.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent3 <- imagenfatigaent3 [,,1] # Deja un solo canal
imagenfatigaent3t <- t(imagenfatigaent3)
imagenfatigaent3xey <- rbind(imagenfatigaent3,imagenfatigaent3t)
imagenfatigaent3xey <- t(imagenfatigaent3xey)
dim(imagenfatigaent3xey)## [1] 200 400imagenfatigaent4 <- read.bitmap("FatigaEnt4.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent4 <- imagenfatigaent4 [,,1] # Deja un solo canal
imagenfatigaent4t <- t(imagenfatigaent4)
imagenfatigaent4xey <- rbind(imagenfatigaent4,imagenfatigaent4t)
imagenfatigaent4xey <- t(imagenfatigaent4xey)
dim(imagenfatigaent4xey)## [1] 200 400imagenfatigaent5 <- read.bitmap("FatigaEnt5.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent5 <- imagenfatigaent5 [,,1] # Deja un solo canal
imagenfatigaent5t <- t(imagenfatigaent5)
imagenfatigaent5xey <- rbind(imagenfatigaent5,imagenfatigaent5t)
imagenfatigaent5xey <- t(imagenfatigaent5xey)
dim(imagenfatigaent5xey)## [1] 200 400imagenfatigaent6 <- read.bitmap("FatigaEnt6.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent6 <- imagenfatigaent6 [,,1] # Deja un solo canal
imagenfatigaent6t <- t(imagenfatigaent6)
imagenfatigaent6xey <- rbind(imagenfatigaent6,imagenfatigaent6t)
imagenfatigaent6xey <- t(imagenfatigaent6xey)
dim(imagenfatigaent6xey)## [1] 200 400imagenfatigaent7 <- read.bitmap("FatigaEnt7.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent7 <- imagenfatigaent7 [,,1] # Deja un solo canal
imagenfatigaent7t <- t(imagenfatigaent7)
imagenfatigaent7xey <- rbind(imagenfatigaent7,imagenfatigaent7t)
imagenfatigaent7xey <- t(imagenfatigaent7xey)
dim(imagenfatigaent7xey)## [1] 200 400imagenfatigaens1 <- read.bitmap("FatigaEns1.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaens1 <- imagenfatigaens1 [,,1] # Deja un solo canal
imagenfatigaens1t <- t(imagenfatigaens1)
imagenfatigaens1xey <- rbind(imagenfatigaens1,imagenfatigaens1t)
imagenfatigaens1xey <- t(imagenfatigaens1xey)
dim(imagenfatigaens1xey)## [1] 200 400imagenfatigaens2 <- read.bitmap("FatigaEns2.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaens2 <- imagenfatigaens2 [,,1] # Deja un solo canal
imagenfatigaens2t <- t(imagenfatigaens2)
imagenfatigaens2xey <- rbind(imagenfatigaens2,imagenfatigaens2t)
imagenfatigaens2xey <- t(imagenfatigaens2xey)
dim(imagenfatigaens2xey)## [1] 200 400imagenfatigaens3 <- read.bitmap("FatigaEns3.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaens3 <- imagenfatigaens3 [,,1] # Deja un solo canal
imagenfatigaens3t <- t(imagenfatigaens3)
imagenfatigaens3xey <- rbind(imagenfatigaens3,imagenfatigaens3t)
imagenfatigaens3xey <- t(imagenfatigaens3xey)
dim(imagenfatigaens3xey)## [1] 200 400imagenfatigaxey <- cbind(imagenfatigaent1xey,imagenfatigaent2xey,imagenfatigaent3xey,imagenfatigaent4xey,imagenfatigaent5xey,imagenfatigaent6xey,imagenfatigaent7xey,imagenfatigaens1xey,imagenfatigaens2xey,imagenfatigaens3xey)
dim(imagenfatigaxey)## [1] 200 40004 Análisis de Componentes Principales de Todas las Curvas en Conjunto
El objetivo es verificar si se forman clusteres por tipo de curvas.
4.1 Suavizamiento conjunto de curvas dúctil, frágil y fatiga
imagenductilfragilfatigaxey <- cbind(imagenductilxey,imagenfragilxey,imagenfatigaxey)
# Convertir en Data Frame
imagenductilfragilfatigaxeydf <- as.data.frame (imagenductilfragilfatigaxey)
attributes(imagenductilfragilfatigaxeydf)## $names
## [1] "V1" "V2" "V3" "V4" "V5" "V6" "V7" "V8"
## [9] "V9" "V10" "V11" "V12" "V13" "V14" "V15" "V16"
## [17] "V17" "V18" "V19" "V20" "V21" "V22" "V23" "V24"
## [25] "V25" "V26" "V27" "V28" "V29" "V30" "V31" "V32"
## [33] "V33" "V34" "V35" "V36" "V37" "V38" "V39" "V40"
## [41] "V41" "V42" "V43" "V44" "V45" "V46" "V47" "V48"
## [49] "V49" "V50" "V51" "V52" "V53" "V54" "V55" "V56"
## [57] "V57" "V58" "V59" "V60" "V61" "V62" "V63" "V64"
## [65] "V65" "V66" "V67" "V68" "V69" "V70" "V71" "V72"
## [73] "V73" "V74" "V75" "V76" "V77" "V78" "V79" "V80"
## [81] "V81" "V82" "V83" "V84" "V85" "V86" "V87" "V88"
## [89] "V89" "V90" "V91" "V92" "V93" "V94" "V95" "V96"
## [97] "V97" "V98" "V99" "V100" "V101" "V102" "V103" "V104"
## [105] "V105" "V106" "V107" "V108" "V109" "V110" "V111" "V112"
## [113] "V113" "V114" "V115" "V116" "V117" "V118" "V119" "V120"
## [121] "V121" "V122" "V123" "V124" "V125" "V126" "V127" "V128"
## [129] "V129" "V130" "V131" "V132" "V133" "V134" "V135" "V136"
## [137] "V137" "V138" "V139" "V140" "V141" "V142" "V143" "V144"
## [145] "V145" "V146" "V147" "V148" "V149" "V150" "V151" "V152"
## [153] "V153" "V154" "V155" "V156" "V157" "V158" "V159" "V160"
## [161] "V161" "V162" "V163" "V164" "V165" "V166" "V167" "V168"
## [169] "V169" "V170" "V171" "V172" "V173" "V174" "V175" "V176"
## [177] "V177" "V178" "V179" "V180" "V181" "V182" "V183" "V184"
## [185] "V185" "V186" "V187" "V188" "V189" "V190" "V191" "V192"
## [193] "V193" "V194" "V195" "V196" "V197" "V198" "V199" "V200"
## [201] "V201" "V202" "V203" "V204" "V205" "V206" "V207" "V208"
## [209] "V209" "V210" "V211" "V212" "V213" "V214" "V215" "V216"
## [217] "V217" "V218" "V219" "V220" "V221" "V222" "V223" "V224"
## [225] "V225" "V226" "V227" "V228" "V229" "V230" "V231" "V232"
## [233] "V233" "V234" "V235" "V236" "V237" "V238" "V239" "V240"
## [241] "V241" "V242" "V243" "V244" "V245" "V246" "V247" "V248"
## [249] "V249" "V250" "V251" "V252" "V253" "V254" "V255" "V256"
## [257] "V257" "V258" "V259" "V260" "V261" "V262" "V263" "V264"
## [265] "V265" "V266" "V267" "V268" "V269" "V270" "V271" "V272"
## [273] "V273" "V274" "V275" "V276" "V277" "V278" "V279" "V280"
## [281] "V281" "V282" "V283" "V284" "V285" "V286" "V287" "V288"
## [289] "V289" "V290" "V291" "V292" "V293" "V294" "V295" "V296"
## [297] "V297" "V298" "V299" "V300" "V301" "V302" "V303" "V304"
## [305] "V305" "V306" "V307" "V308" "V309" "V310" "V311" "V312"
## [313] "V313" "V314" "V315" "V316" "V317" "V318" "V319" "V320"
## [321] "V321" "V322" "V323" "V324" "V325" "V326" "V327" "V328"
## [329] "V329" "V330" "V331" "V332" "V333" "V334" "V335" "V336"
## [337] "V337" "V338" "V339" "V340" "V341" "V342" "V343" "V344"
## [345] "V345" "V346" "V347" "V348" "V349" "V350" "V351" "V352"
## [353] "V353" "V354" "V355" "V356" "V357" "V358" "V359" "V360"
## [361] "V361" "V362" "V363" "V364" "V365" "V366" "V367" "V368"
## [369] "V369" "V370" "V371" "V372" "V373" "V374" "V375" "V376"
## [377] "V377" "V378" "V379" "V380" "V381" "V382" "V383" "V384"
## [385] "V385" "V386" "V387" "V388" "V389" "V390" "V391" "V392"
## [393] "V393" "V394" "V395" "V396" "V397" "V398" "V399" "V400"
## [401] "V401" "V402" "V403" "V404" "V405" "V406" "V407" "V408"
## [409] "V409" "V410" "V411" "V412" "V413" "V414" "V415" "V416"
## [417] "V417" "V418" "V419" "V420" "V421" "V422" "V423" "V424"
## [425] "V425" "V426" "V427" "V428" "V429" "V430" "V431" "V432"
## [433] "V433" "V434" "V435" "V436" "V437" "V438" "V439" "V440"
## [441] "V441" "V442" "V443" "V444" "V445" "V446" "V447" "V448"
## [449] "V449" "V450" "V451" "V452" "V453" "V454" "V455" "V456"
## [457] "V457" "V458" "V459" "V460" "V461" "V462" "V463" "V464"
## [465] "V465" "V466" "V467" "V468" "V469" "V470" "V471" "V472"
## [473] "V473" "V474" "V475" "V476" "V477" "V478" "V479" "V480"
## [481] "V481" "V482" "V483" "V484" "V485" "V486" "V487" "V488"
## [489] "V489" "V490" "V491" "V492" "V493" "V494" "V495" "V496"
## [497] "V497" "V498" "V499" "V500" "V501" "V502" "V503" "V504"
## [505] "V505" "V506" "V507" "V508" "V509" "V510" "V511" "V512"
## [513] "V513" "V514" "V515" "V516" "V517" "V518" "V519" "V520"
## [521] "V521" "V522" "V523" "V524" "V525" "V526" "V527" "V528"
## [529] "V529" "V530" "V531" "V532" "V533" "V534" "V535" "V536"
## [537] "V537" "V538" "V539" "V540" "V541" "V542" "V543" "V544"
## [545] "V545" "V546" "V547" "V548" "V549" "V550" "V551" "V552"
## [553] "V553" "V554" "V555" "V556" "V557" "V558" "V559" "V560"
## [561] "V561" "V562" "V563" "V564" "V565" "V566" "V567" "V568"
## [569] "V569" "V570" "V571" "V572" "V573" "V574" "V575" "V576"
## [577] "V577" "V578" "V579" "V580" "V581" "V582" "V583" "V584"
## [585] "V585" "V586" "V587" "V588" "V589" "V590" "V591" "V592"
## [593] "V593" "V594" "V595" "V596" "V597" "V598" "V599" "V600"
## [601] "V601" "V602" "V603" "V604" "V605" "V606" "V607" "V608"
## [609] "V609" "V610" "V611" "V612" "V613" "V614" "V615" "V616"
## [617] "V617" "V618" "V619" "V620" "V621" "V622" "V623" "V624"
## [625] "V625" "V626" "V627" "V628" "V629" "V630" "V631" "V632"
## [633] "V633" "V634" "V635" "V636" "V637" "V638" "V639" "V640"
## [641] "V641" "V642" "V643" "V644" "V645" "V646" "V647" "V648"
## [649] "V649" "V650" "V651" "V652" "V653" "V654" "V655" "V656"
## [657] "V657" "V658" "V659" "V660" "V661" "V662" "V663" "V664"
## [665] "V665" "V666" "V667" "V668" "V669" "V670" "V671" "V672"
## [673] "V673" "V674" "V675" "V676" "V677" "V678" "V679" "V680"
## [681] "V681" "V682" "V683" "V684" "V685" "V686" "V687" "V688"
## [689] "V689" "V690" "V691" "V692" "V693" "V694" "V695" "V696"
## [697] "V697" "V698" "V699" "V700" "V701" "V702" "V703" "V704"
## [705] "V705" "V706" "V707" "V708" "V709" "V710" "V711" "V712"
## [713] "V713" "V714" "V715" "V716" "V717" "V718" "V719" "V720"
## [721] "V721" "V722" "V723" "V724" "V725" "V726" "V727" "V728"
## [729] "V729" "V730" "V731" "V732" "V733" "V734" "V735" "V736"
## [737] "V737" "V738" "V739" "V740" "V741" "V742" "V743" "V744"
## [745] "V745" "V746" "V747" "V748" "V749" "V750" "V751" "V752"
## [753] "V753" "V754" "V755" "V756" "V757" "V758" "V759" "V760"
## [761] "V761" "V762" "V763" "V764" "V765" "V766" "V767" "V768"
## [769] "V769" "V770" "V771" "V772" "V773" "V774" "V775" "V776"
## [777] "V777" "V778" "V779" "V780" "V781" "V782" "V783" "V784"
## [785] "V785" "V786" "V787" "V788" "V789" "V790" "V791" "V792"
## [793] "V793" "V794" "V795" "V796" "V797" "V798" "V799" "V800"
## [801] "V801" "V802" "V803" "V804" "V805" "V806" "V807" "V808"
## [809] "V809" "V810" "V811" "V812" "V813" "V814" "V815" "V816"
## [817] "V817" "V818" "V819" "V820" "V821" "V822" "V823" "V824"
## [825] "V825" "V826" "V827" "V828" "V829" "V830" "V831" "V832"
## [833] "V833" "V834" "V835" "V836" "V837" "V838" "V839" "V840"
## [841] "V841" "V842" "V843" "V844" "V845" "V846" "V847" "V848"
## [849] "V849" "V850" "V851" "V852" "V853" "V854" "V855" "V856"
## [857] "V857" "V858" "V859" "V860" "V861" "V862" "V863" "V864"
## [865] "V865" "V866" "V867" "V868" "V869" "V870" "V871" "V872"
## [873] "V873" "V874" "V875" "V876" "V877" "V878" "V879" "V880"
## [881] "V881" "V882" "V883" "V884" "V885" "V886" "V887" "V888"
## [889] "V889" "V890" "V891" "V892" "V893" "V894" "V895" "V896"
## [897] "V897" "V898" "V899" "V900" "V901" "V902" "V903" "V904"
## [905] "V905" "V906" "V907" "V908" "V909" "V910" "V911" "V912"
## [913] "V913" "V914" "V915" "V916" "V917" "V918" "V919" "V920"
## [921] "V921" "V922" "V923" "V924" "V925" "V926" "V927" "V928"
## [929] "V929" "V930" "V931" "V932" "V933" "V934" "V935" "V936"
## [937] "V937" "V938" "V939" "V940" "V941" "V942" "V943" "V944"
## [945] "V945" "V946" "V947" "V948" "V949" "V950" "V951" "V952"
## [953] "V953" "V954" "V955" "V956" "V957" "V958" "V959" "V960"
## [961] "V961" "V962" "V963" "V964" "V965" "V966" "V967" "V968"
## [969] "V969" "V970" "V971" "V972" "V973" "V974" "V975" "V976"
## [977] "V977" "V978" "V979" "V980" "V981" "V982" "V983" "V984"
## [985] "V985" "V986" "V987" "V988" "V989" "V990" "V991" "V992"
## [993] "V993" "V994" "V995" "V996" "V997" "V998" "V999" "V1000"
## [1001] "V1001" "V1002" "V1003" "V1004" "V1005" "V1006" "V1007" "V1008"
## [1009] "V1009" "V1010" "V1011" "V1012" "V1013" "V1014" "V1015" "V1016"
## [1017] "V1017" "V1018" "V1019" "V1020" "V1021" "V1022" "V1023" "V1024"
## [1025] "V1025" "V1026" "V1027" "V1028" "V1029" "V1030" "V1031" "V1032"
## [1033] "V1033" "V1034" "V1035" "V1036" "V1037" "V1038" "V1039" "V1040"
## [1041] "V1041" "V1042" "V1043" "V1044" "V1045" "V1046" "V1047" "V1048"
## [1049] "V1049" "V1050" "V1051" "V1052" "V1053" "V1054" "V1055" "V1056"
## [1057] "V1057" "V1058" "V1059" "V1060" "V1061" "V1062" "V1063" "V1064"
## [1065] "V1065" "V1066" "V1067" "V1068" "V1069" "V1070" "V1071" "V1072"
## [1073] "V1073" "V1074" "V1075" "V1076" "V1077" "V1078" "V1079" "V1080"
## [1081] "V1081" "V1082" "V1083" "V1084" "V1085" "V1086" "V1087" "V1088"
## [1089] "V1089" "V1090" "V1091" "V1092" "V1093" "V1094" "V1095" "V1096"
## [1097] "V1097" "V1098" "V1099" "V1100" "V1101" "V1102" "V1103" "V1104"
## [1105] "V1105" "V1106" "V1107" "V1108" "V1109" "V1110" "V1111" "V1112"
## [1113] "V1113" "V1114" "V1115" "V1116" "V1117" "V1118" "V1119" "V1120"
## [1121] "V1121" "V1122" "V1123" "V1124" "V1125" "V1126" "V1127" "V1128"
## [1129] "V1129" "V1130" "V1131" "V1132" "V1133" "V1134" "V1135" "V1136"
## [1137] "V1137" "V1138" "V1139" "V1140" "V1141" "V1142" "V1143" "V1144"
## [1145] "V1145" "V1146" "V1147" "V1148" "V1149" "V1150" "V1151" "V1152"
## [1153] "V1153" "V1154" "V1155" "V1156" "V1157" "V1158" "V1159" "V1160"
## [1161] "V1161" "V1162" "V1163" "V1164" "V1165" "V1166" "V1167" "V1168"
## [1169] "V1169" "V1170" "V1171" "V1172" "V1173" "V1174" "V1175" "V1176"
## [1177] "V1177" "V1178" "V1179" "V1180" "V1181" "V1182" "V1183" "V1184"
## [1185] "V1185" "V1186" "V1187" "V1188" "V1189" "V1190" "V1191" "V1192"
## [1193] "V1193" "V1194" "V1195" "V1196" "V1197" "V1198" "V1199" "V1200"
## [1201] "V1201" "V1202" "V1203" "V1204" "V1205" "V1206" "V1207" "V1208"
## [1209] "V1209" "V1210" "V1211" "V1212" "V1213" "V1214" "V1215" "V1216"
## [1217] "V1217" "V1218" "V1219" "V1220" "V1221" "V1222" "V1223" "V1224"
## [1225] "V1225" "V1226" "V1227" "V1228" "V1229" "V1230" "V1231" "V1232"
## [1233] "V1233" "V1234" "V1235" "V1236" "V1237" "V1238" "V1239" "V1240"
## [1241] "V1241" "V1242" "V1243" "V1244" "V1245" "V1246" "V1247" "V1248"
## [1249] "V1249" "V1250" "V1251" "V1252" "V1253" "V1254" "V1255" "V1256"
## [1257] "V1257" "V1258" "V1259" "V1260" "V1261" "V1262" "V1263" "V1264"
## [1265] "V1265" "V1266" "V1267" "V1268" "V1269" "V1270" "V1271" "V1272"
## [1273] "V1273" "V1274" "V1275" "V1276" "V1277" "V1278" "V1279" "V1280"
## [1281] "V1281" "V1282" "V1283" "V1284" "V1285" "V1286" "V1287" "V1288"
## [1289] "V1289" "V1290" "V1291" "V1292" "V1293" "V1294" "V1295" "V1296"
## [1297] "V1297" "V1298" "V1299" "V1300" "V1301" "V1302" "V1303" "V1304"
## [1305] "V1305" "V1306" "V1307" "V1308" "V1309" "V1310" "V1311" "V1312"
## [1313] "V1313" "V1314" "V1315" "V1316" "V1317" "V1318" "V1319" "V1320"
## [1321] "V1321" "V1322" "V1323" "V1324" "V1325" "V1326" "V1327" "V1328"
## [1329] "V1329" "V1330" "V1331" "V1332" "V1333" "V1334" "V1335" "V1336"
## [1337] "V1337" "V1338" "V1339" "V1340" "V1341" "V1342" "V1343" "V1344"
## [1345] "V1345" "V1346" "V1347" "V1348" "V1349" "V1350" "V1351" "V1352"
## [1353] "V1353" "V1354" "V1355" "V1356" "V1357" "V1358" "V1359" "V1360"
## [1361] "V1361" "V1362" "V1363" "V1364" "V1365" "V1366" "V1367" "V1368"
## [1369] "V1369" "V1370" "V1371" "V1372" "V1373" "V1374" "V1375" "V1376"
## [1377] "V1377" "V1378" "V1379" "V1380" "V1381" "V1382" "V1383" "V1384"
## [1385] "V1385" "V1386" "V1387" "V1388" "V1389" "V1390" "V1391" "V1392"
## [1393] "V1393" "V1394" "V1395" "V1396" "V1397" "V1398" "V1399" "V1400"
## [1401] "V1401" "V1402" "V1403" "V1404" "V1405" "V1406" "V1407" "V1408"
## [1409] "V1409" "V1410" "V1411" "V1412" "V1413" "V1414" "V1415" "V1416"
## [1417] "V1417" "V1418" "V1419" "V1420" "V1421" "V1422" "V1423" "V1424"
## [1425] "V1425" "V1426" "V1427" "V1428" "V1429" "V1430" "V1431" "V1432"
## [1433] "V1433" "V1434" "V1435" "V1436" "V1437" "V1438" "V1439" "V1440"
## [1441] "V1441" "V1442" "V1443" "V1444" "V1445" "V1446" "V1447" "V1448"
## [1449] "V1449" "V1450" "V1451" "V1452" "V1453" "V1454" "V1455" "V1456"
## [1457] "V1457" "V1458" "V1459" "V1460" "V1461" "V1462" "V1463" "V1464"
## [1465] "V1465" "V1466" "V1467" "V1468" "V1469" "V1470" "V1471" "V1472"
## [1473] "V1473" "V1474" "V1475" "V1476" "V1477" "V1478" "V1479" "V1480"
## [1481] "V1481" "V1482" "V1483" "V1484" "V1485" "V1486" "V1487" "V1488"
## [1489] "V1489" "V1490" "V1491" "V1492" "V1493" "V1494" "V1495" "V1496"
## [1497] "V1497" "V1498" "V1499" "V1500" "V1501" "V1502" "V1503" "V1504"
## [1505] "V1505" "V1506" "V1507" "V1508" "V1509" "V1510" "V1511" "V1512"
## [1513] "V1513" "V1514" "V1515" "V1516" "V1517" "V1518" "V1519" "V1520"
## [1521] "V1521" "V1522" "V1523" "V1524" "V1525" "V1526" "V1527" "V1528"
## [1529] "V1529" "V1530" "V1531" "V1532" "V1533" "V1534" "V1535" "V1536"
## [1537] "V1537" "V1538" "V1539" "V1540" "V1541" "V1542" "V1543" "V1544"
## [1545] "V1545" "V1546" "V1547" "V1548" "V1549" "V1550" "V1551" "V1552"
## [1553] "V1553" "V1554" "V1555" "V1556" "V1557" "V1558" "V1559" "V1560"
## [1561] "V1561" "V1562" "V1563" "V1564" "V1565" "V1566" "V1567" "V1568"
## [1569] "V1569" "V1570" "V1571" "V1572" "V1573" "V1574" "V1575" "V1576"
## [1577] "V1577" "V1578" "V1579" "V1580" "V1581" "V1582" "V1583" "V1584"
## [1585] "V1585" "V1586" "V1587" "V1588" "V1589" "V1590" "V1591" "V1592"
## [1593] "V1593" "V1594" "V1595" "V1596" "V1597" "V1598" "V1599" "V1600"
## [1601] "V1601" "V1602" "V1603" "V1604" "V1605" "V1606" "V1607" "V1608"
## [1609] "V1609" "V1610" "V1611" "V1612" "V1613" "V1614" "V1615" "V1616"
## [1617] "V1617" "V1618" "V1619" "V1620" "V1621" "V1622" "V1623" "V1624"
## [1625] "V1625" "V1626" "V1627" "V1628" "V1629" "V1630" "V1631" "V1632"
## [1633] "V1633" "V1634" "V1635" "V1636" "V1637" "V1638" "V1639" "V1640"
## [1641] "V1641" "V1642" "V1643" "V1644" "V1645" "V1646" "V1647" "V1648"
## [1649] "V1649" "V1650" "V1651" "V1652" "V1653" "V1654" "V1655" "V1656"
## [1657] "V1657" "V1658" "V1659" "V1660" "V1661" "V1662" "V1663" "V1664"
## [1665] "V1665" "V1666" "V1667" "V1668" "V1669" "V1670" "V1671" "V1672"
## [1673] "V1673" "V1674" "V1675" "V1676" "V1677" "V1678" "V1679" "V1680"
## [1681] "V1681" "V1682" "V1683" "V1684" "V1685" "V1686" "V1687" "V1688"
## [1689] "V1689" "V1690" "V1691" "V1692" "V1693" "V1694" "V1695" "V1696"
## [1697] "V1697" "V1698" "V1699" "V1700" "V1701" "V1702" "V1703" "V1704"
## [1705] "V1705" "V1706" "V1707" "V1708" "V1709" "V1710" "V1711" "V1712"
## [1713] "V1713" "V1714" "V1715" "V1716" "V1717" "V1718" "V1719" "V1720"
## [1721] "V1721" "V1722" "V1723" "V1724" "V1725" "V1726" "V1727" "V1728"
## [1729] "V1729" "V1730" "V1731" "V1732" "V1733" "V1734" "V1735" "V1736"
## [1737] "V1737" "V1738" "V1739" "V1740" "V1741" "V1742" "V1743" "V1744"
## [1745] "V1745" "V1746" "V1747" "V1748" "V1749" "V1750" "V1751" "V1752"
## [1753] "V1753" "V1754" "V1755" "V1756" "V1757" "V1758" "V1759" "V1760"
## [1761] "V1761" "V1762" "V1763" "V1764" "V1765" "V1766" "V1767" "V1768"
## [1769] "V1769" "V1770" "V1771" "V1772" "V1773" "V1774" "V1775" "V1776"
## [1777] "V1777" "V1778" "V1779" "V1780" "V1781" "V1782" "V1783" "V1784"
## [1785] "V1785" "V1786" "V1787" "V1788" "V1789" "V1790" "V1791" "V1792"
## [1793] "V1793" "V1794" "V1795" "V1796" "V1797" "V1798" "V1799" "V1800"
## [1801] "V1801" "V1802" "V1803" "V1804" "V1805" "V1806" "V1807" "V1808"
## [1809] "V1809" "V1810" "V1811" "V1812" "V1813" "V1814" "V1815" "V1816"
## [1817] "V1817" "V1818" "V1819" "V1820" "V1821" "V1822" "V1823" "V1824"
## [1825] "V1825" "V1826" "V1827" "V1828" "V1829" "V1830" "V1831" "V1832"
## [1833] "V1833" "V1834" "V1835" "V1836" "V1837" "V1838" "V1839" "V1840"
## [1841] "V1841" "V1842" "V1843" "V1844" "V1845" "V1846" "V1847" "V1848"
## [1849] "V1849" "V1850" "V1851" "V1852" "V1853" "V1854" "V1855" "V1856"
## [1857] "V1857" "V1858" "V1859" "V1860" "V1861" "V1862" "V1863" "V1864"
## [1865] "V1865" "V1866" "V1867" "V1868" "V1869" "V1870" "V1871" "V1872"
## [1873] "V1873" "V1874" "V1875" "V1876" "V1877" "V1878" "V1879" "V1880"
## [1881] "V1881" "V1882" "V1883" "V1884" "V1885" "V1886" "V1887" "V1888"
## [1889] "V1889" "V1890" "V1891" "V1892" "V1893" "V1894" "V1895" "V1896"
## [1897] "V1897" "V1898" "V1899" "V1900" "V1901" "V1902" "V1903" "V1904"
## [1905] "V1905" "V1906" "V1907" "V1908" "V1909" "V1910" "V1911" "V1912"
## [1913] "V1913" "V1914" "V1915" "V1916" "V1917" "V1918" "V1919" "V1920"
## [1921] "V1921" "V1922" "V1923" "V1924" "V1925" "V1926" "V1927" "V1928"
## [1929] "V1929" "V1930" "V1931" "V1932" "V1933" "V1934" "V1935" "V1936"
## [1937] "V1937" "V1938" "V1939" "V1940" "V1941" "V1942" "V1943" "V1944"
## [1945] "V1945" "V1946" "V1947" "V1948" "V1949" "V1950" "V1951" "V1952"
## [1953] "V1953" "V1954" "V1955" "V1956" "V1957" "V1958" "V1959" "V1960"
## [1961] "V1961" "V1962" "V1963" "V1964" "V1965" "V1966" "V1967" "V1968"
## [1969] "V1969" "V1970" "V1971" "V1972" "V1973" "V1974" "V1975" "V1976"
## [1977] "V1977" "V1978" "V1979" "V1980" "V1981" "V1982" "V1983" "V1984"
## [1985] "V1985" "V1986" "V1987" "V1988" "V1989" "V1990" "V1991" "V1992"
## [1993] "V1993" "V1994" "V1995" "V1996" "V1997" "V1998" "V1999" "V2000"
## [2001] "V2001" "V2002" "V2003" "V2004" "V2005" "V2006" "V2007" "V2008"
## [2009] "V2009" "V2010" "V2011" "V2012" "V2013" "V2014" "V2015" "V2016"
## [2017] "V2017" "V2018" "V2019" "V2020" "V2021" "V2022" "V2023" "V2024"
## [2025] "V2025" "V2026" "V2027" "V2028" "V2029" "V2030" "V2031" "V2032"
## [2033] "V2033" "V2034" "V2035" "V2036" "V2037" "V2038" "V2039" "V2040"
## [2041] "V2041" "V2042" "V2043" "V2044" "V2045" "V2046" "V2047" "V2048"
## [2049] "V2049" "V2050" "V2051" "V2052" "V2053" "V2054" "V2055" "V2056"
## [2057] "V2057" "V2058" "V2059" "V2060" "V2061" "V2062" "V2063" "V2064"
## [2065] "V2065" "V2066" "V2067" "V2068" "V2069" "V2070" "V2071" "V2072"
## [2073] "V2073" "V2074" "V2075" "V2076" "V2077" "V2078" "V2079" "V2080"
## [2081] "V2081" "V2082" "V2083" "V2084" "V2085" "V2086" "V2087" "V2088"
## [2089] "V2089" "V2090" "V2091" "V2092" "V2093" "V2094" "V2095" "V2096"
## [2097] "V2097" "V2098" "V2099" "V2100" "V2101" "V2102" "V2103" "V2104"
## [2105] "V2105" "V2106" "V2107" "V2108" "V2109" "V2110" "V2111" "V2112"
## [2113] "V2113" "V2114" "V2115" "V2116" "V2117" "V2118" "V2119" "V2120"
## [2121] "V2121" "V2122" "V2123" "V2124" "V2125" "V2126" "V2127" "V2128"
## [2129] "V2129" "V2130" "V2131" "V2132" "V2133" "V2134" "V2135" "V2136"
## [2137] "V2137" "V2138" "V2139" "V2140" "V2141" "V2142" "V2143" "V2144"
## [2145] "V2145" "V2146" "V2147" "V2148" "V2149" "V2150" "V2151" "V2152"
## [2153] "V2153" "V2154" "V2155" "V2156" "V2157" "V2158" "V2159" "V2160"
## [2161] "V2161" "V2162" "V2163" "V2164" "V2165" "V2166" "V2167" "V2168"
## [2169] "V2169" "V2170" "V2171" "V2172" "V2173" "V2174" "V2175" "V2176"
## [2177] "V2177" "V2178" "V2179" "V2180" "V2181" "V2182" "V2183" "V2184"
## [2185] "V2185" "V2186" "V2187" "V2188" "V2189" "V2190" "V2191" "V2192"
## [2193] "V2193" "V2194" "V2195" "V2196" "V2197" "V2198" "V2199" "V2200"
## [2201] "V2201" "V2202" "V2203" "V2204" "V2205" "V2206" "V2207" "V2208"
## [2209] "V2209" "V2210" "V2211" "V2212" "V2213" "V2214" "V2215" "V2216"
## [2217] "V2217" "V2218" "V2219" "V2220" "V2221" "V2222" "V2223" "V2224"
## [2225] "V2225" "V2226" "V2227" "V2228" "V2229" "V2230" "V2231" "V2232"
## [2233] "V2233" "V2234" "V2235" "V2236" "V2237" "V2238" "V2239" "V2240"
## [2241] "V2241" "V2242" "V2243" "V2244" "V2245" "V2246" "V2247" "V2248"
## [2249] "V2249" "V2250" "V2251" "V2252" "V2253" "V2254" "V2255" "V2256"
## [2257] "V2257" "V2258" "V2259" "V2260" "V2261" "V2262" "V2263" "V2264"
## [2265] "V2265" "V2266" "V2267" "V2268" "V2269" "V2270" "V2271" "V2272"
## [2273] "V2273" "V2274" "V2275" "V2276" "V2277" "V2278" "V2279" "V2280"
## [2281] "V2281" "V2282" "V2283" "V2284" "V2285" "V2286" "V2287" "V2288"
## [2289] "V2289" "V2290" "V2291" "V2292" "V2293" "V2294" "V2295" "V2296"
## [2297] "V2297" "V2298" "V2299" "V2300" "V2301" "V2302" "V2303" "V2304"
## [2305] "V2305" "V2306" "V2307" "V2308" "V2309" "V2310" "V2311" "V2312"
## [2313] "V2313" "V2314" "V2315" "V2316" "V2317" "V2318" "V2319" "V2320"
## [2321] "V2321" "V2322" "V2323" "V2324" "V2325" "V2326" "V2327" "V2328"
## [2329] "V2329" "V2330" "V2331" "V2332" "V2333" "V2334" "V2335" "V2336"
## [2337] "V2337" "V2338" "V2339" "V2340" "V2341" "V2342" "V2343" "V2344"
## [2345] "V2345" "V2346" "V2347" "V2348" "V2349" "V2350" "V2351" "V2352"
## [2353] "V2353" "V2354" "V2355" "V2356" "V2357" "V2358" "V2359" "V2360"
## [2361] "V2361" "V2362" "V2363" "V2364" "V2365" "V2366" "V2367" "V2368"
## [2369] "V2369" "V2370" "V2371" "V2372" "V2373" "V2374" "V2375" "V2376"
## [2377] "V2377" "V2378" "V2379" "V2380" "V2381" "V2382" "V2383" "V2384"
## [2385] "V2385" "V2386" "V2387" "V2388" "V2389" "V2390" "V2391" "V2392"
## [2393] "V2393" "V2394" "V2395" "V2396" "V2397" "V2398" "V2399" "V2400"
## [2401] "V2401" "V2402" "V2403" "V2404" "V2405" "V2406" "V2407" "V2408"
## [2409] "V2409" "V2410" "V2411" "V2412" "V2413" "V2414" "V2415" "V2416"
## [2417] "V2417" "V2418" "V2419" "V2420" "V2421" "V2422" "V2423" "V2424"
## [2425] "V2425" "V2426" "V2427" "V2428" "V2429" "V2430" "V2431" "V2432"
## [2433] "V2433" "V2434" "V2435" "V2436" "V2437" "V2438" "V2439" "V2440"
## [2441] "V2441" "V2442" "V2443" "V2444" "V2445" "V2446" "V2447" "V2448"
## [2449] "V2449" "V2450" "V2451" "V2452" "V2453" "V2454" "V2455" "V2456"
## [2457] "V2457" "V2458" "V2459" "V2460" "V2461" "V2462" "V2463" "V2464"
## [2465] "V2465" "V2466" "V2467" "V2468" "V2469" "V2470" "V2471" "V2472"
## [2473] "V2473" "V2474" "V2475" "V2476" "V2477" "V2478" "V2479" "V2480"
## [2481] "V2481" "V2482" "V2483" "V2484" "V2485" "V2486" "V2487" "V2488"
## [2489] "V2489" "V2490" "V2491" "V2492" "V2493" "V2494" "V2495" "V2496"
## [2497] "V2497" "V2498" "V2499" "V2500" "V2501" "V2502" "V2503" "V2504"
## [2505] "V2505" "V2506" "V2507" "V2508" "V2509" "V2510" "V2511" "V2512"
## [2513] "V2513" "V2514" "V2515" "V2516" "V2517" "V2518" "V2519" "V2520"
## [2521] "V2521" "V2522" "V2523" "V2524" "V2525" "V2526" "V2527" "V2528"
## [2529] "V2529" "V2530" "V2531" "V2532" "V2533" "V2534" "V2535" "V2536"
## [2537] "V2537" "V2538" "V2539" "V2540" "V2541" "V2542" "V2543" "V2544"
## [2545] "V2545" "V2546" "V2547" "V2548" "V2549" "V2550" "V2551" "V2552"
## [2553] "V2553" "V2554" "V2555" "V2556" "V2557" "V2558" "V2559" "V2560"
## [2561] "V2561" "V2562" "V2563" "V2564" "V2565" "V2566" "V2567" "V2568"
## [2569] "V2569" "V2570" "V2571" "V2572" "V2573" "V2574" "V2575" "V2576"
## [2577] "V2577" "V2578" "V2579" "V2580" "V2581" "V2582" "V2583" "V2584"
## [2585] "V2585" "V2586" "V2587" "V2588" "V2589" "V2590" "V2591" "V2592"
## [2593] "V2593" "V2594" "V2595" "V2596" "V2597" "V2598" "V2599" "V2600"
## [2601] "V2601" "V2602" "V2603" "V2604" "V2605" "V2606" "V2607" "V2608"
## [2609] "V2609" "V2610" "V2611" "V2612" "V2613" "V2614" "V2615" "V2616"
## [2617] "V2617" "V2618" "V2619" "V2620" "V2621" "V2622" "V2623" "V2624"
## [2625] "V2625" "V2626" "V2627" "V2628" "V2629" "V2630" "V2631" "V2632"
## [2633] "V2633" "V2634" "V2635" "V2636" "V2637" "V2638" "V2639" "V2640"
## [2641] "V2641" "V2642" "V2643" "V2644" "V2645" "V2646" "V2647" "V2648"
## [2649] "V2649" "V2650" "V2651" "V2652" "V2653" "V2654" "V2655" "V2656"
## [2657] "V2657" "V2658" "V2659" "V2660" "V2661" "V2662" "V2663" "V2664"
## [2665] "V2665" "V2666" "V2667" "V2668" "V2669" "V2670" "V2671" "V2672"
## [2673] "V2673" "V2674" "V2675" "V2676" "V2677" "V2678" "V2679" "V2680"
## [2681] "V2681" "V2682" "V2683" "V2684" "V2685" "V2686" "V2687" "V2688"
## [2689] "V2689" "V2690" "V2691" "V2692" "V2693" "V2694" "V2695" "V2696"
## [2697] "V2697" "V2698" "V2699" "V2700" "V2701" "V2702" "V2703" "V2704"
## [2705] "V2705" "V2706" "V2707" "V2708" "V2709" "V2710" "V2711" "V2712"
## [2713] "V2713" "V2714" "V2715" "V2716" "V2717" "V2718" "V2719" "V2720"
## [2721] "V2721" "V2722" "V2723" "V2724" "V2725" "V2726" "V2727" "V2728"
## [2729] "V2729" "V2730" "V2731" "V2732" "V2733" "V2734" "V2735" "V2736"
## [2737] "V2737" "V2738" "V2739" "V2740" "V2741" "V2742" "V2743" "V2744"
## [2745] "V2745" "V2746" "V2747" "V2748" "V2749" "V2750" "V2751" "V2752"
## [2753] "V2753" "V2754" "V2755" "V2756" "V2757" "V2758" "V2759" "V2760"
## [2761] "V2761" "V2762" "V2763" "V2764" "V2765" "V2766" "V2767" "V2768"
## [2769] "V2769" "V2770" "V2771" "V2772" "V2773" "V2774" "V2775" "V2776"
## [2777] "V2777" "V2778" "V2779" "V2780" "V2781" "V2782" "V2783" "V2784"
## [2785] "V2785" "V2786" "V2787" "V2788" "V2789" "V2790" "V2791" "V2792"
## [2793] "V2793" "V2794" "V2795" "V2796" "V2797" "V2798" "V2799" "V2800"
## [2801] "V2801" "V2802" "V2803" "V2804" "V2805" "V2806" "V2807" "V2808"
## [2809] "V2809" "V2810" "V2811" "V2812" "V2813" "V2814" "V2815" "V2816"
## [2817] "V2817" "V2818" "V2819" "V2820" "V2821" "V2822" "V2823" "V2824"
## [2825] "V2825" "V2826" "V2827" "V2828" "V2829" "V2830" "V2831" "V2832"
## [2833] "V2833" "V2834" "V2835" "V2836" "V2837" "V2838" "V2839" "V2840"
## [2841] "V2841" "V2842" "V2843" "V2844" "V2845" "V2846" "V2847" "V2848"
## [2849] "V2849" "V2850" "V2851" "V2852" "V2853" "V2854" "V2855" "V2856"
## [2857] "V2857" "V2858" "V2859" "V2860" "V2861" "V2862" "V2863" "V2864"
## [2865] "V2865" "V2866" "V2867" "V2868" "V2869" "V2870" "V2871" "V2872"
## [2873] "V2873" "V2874" "V2875" "V2876" "V2877" "V2878" "V2879" "V2880"
## [2881] "V2881" "V2882" "V2883" "V2884" "V2885" "V2886" "V2887" "V2888"
## [2889] "V2889" "V2890" "V2891" "V2892" "V2893" "V2894" "V2895" "V2896"
## [2897] "V2897" "V2898" "V2899" "V2900" "V2901" "V2902" "V2903" "V2904"
## [2905] "V2905" "V2906" "V2907" "V2908" "V2909" "V2910" "V2911" "V2912"
## [2913] "V2913" "V2914" "V2915" "V2916" "V2917" "V2918" "V2919" "V2920"
## [2921] "V2921" "V2922" "V2923" "V2924" "V2925" "V2926" "V2927" "V2928"
## [2929] "V2929" "V2930" "V2931" "V2932" "V2933" "V2934" "V2935" "V2936"
## [2937] "V2937" "V2938" "V2939" "V2940" "V2941" "V2942" "V2943" "V2944"
## [2945] "V2945" "V2946" "V2947" "V2948" "V2949" "V2950" "V2951" "V2952"
## [2953] "V2953" "V2954" "V2955" "V2956" "V2957" "V2958" "V2959" "V2960"
## [2961] "V2961" "V2962" "V2963" "V2964" "V2965" "V2966" "V2967" "V2968"
## [2969] "V2969" "V2970" "V2971" "V2972" "V2973" "V2974" "V2975" "V2976"
## [2977] "V2977" "V2978" "V2979" "V2980" "V2981" "V2982" "V2983" "V2984"
## [2985] "V2985" "V2986" "V2987" "V2988" "V2989" "V2990" "V2991" "V2992"
## [2993] "V2993" "V2994" "V2995" "V2996" "V2997" "V2998" "V2999" "V3000"
## [3001] "V3001" "V3002" "V3003" "V3004" "V3005" "V3006" "V3007" "V3008"
## [3009] "V3009" "V3010" "V3011" "V3012" "V3013" "V3014" "V3015" "V3016"
## [3017] "V3017" "V3018" "V3019" "V3020" "V3021" "V3022" "V3023" "V3024"
## [3025] "V3025" "V3026" "V3027" "V3028" "V3029" "V3030" "V3031" "V3032"
## [3033] "V3033" "V3034" "V3035" "V3036" "V3037" "V3038" "V3039" "V3040"
## [3041] "V3041" "V3042" "V3043" "V3044" "V3045" "V3046" "V3047" "V3048"
## [3049] "V3049" "V3050" "V3051" "V3052" "V3053" "V3054" "V3055" "V3056"
## [3057] "V3057" "V3058" "V3059" "V3060" "V3061" "V3062" "V3063" "V3064"
## [3065] "V3065" "V3066" "V3067" "V3068" "V3069" "V3070" "V3071" "V3072"
## [3073] "V3073" "V3074" "V3075" "V3076" "V3077" "V3078" "V3079" "V3080"
## [3081] "V3081" "V3082" "V3083" "V3084" "V3085" "V3086" "V3087" "V3088"
## [3089] "V3089" "V3090" "V3091" "V3092" "V3093" "V3094" "V3095" "V3096"
## [3097] "V3097" "V3098" "V3099" "V3100" "V3101" "V3102" "V3103" "V3104"
## [3105] "V3105" "V3106" "V3107" "V3108" "V3109" "V3110" "V3111" "V3112"
## [3113] "V3113" "V3114" "V3115" "V3116" "V3117" "V3118" "V3119" "V3120"
## [3121] "V3121" "V3122" "V3123" "V3124" "V3125" "V3126" "V3127" "V3128"
## [3129] "V3129" "V3130" "V3131" "V3132" "V3133" "V3134" "V3135" "V3136"
## [3137] "V3137" "V3138" "V3139" "V3140" "V3141" "V3142" "V3143" "V3144"
## [3145] "V3145" "V3146" "V3147" "V3148" "V3149" "V3150" "V3151" "V3152"
## [3153] "V3153" "V3154" "V3155" "V3156" "V3157" "V3158" "V3159" "V3160"
## [3161] "V3161" "V3162" "V3163" "V3164" "V3165" "V3166" "V3167" "V3168"
## [3169] "V3169" "V3170" "V3171" "V3172" "V3173" "V3174" "V3175" "V3176"
## [3177] "V3177" "V3178" "V3179" "V3180" "V3181" "V3182" "V3183" "V3184"
## [3185] "V3185" "V3186" "V3187" "V3188" "V3189" "V3190" "V3191" "V3192"
## [3193] "V3193" "V3194" "V3195" "V3196" "V3197" "V3198" "V3199" "V3200"
## [3201] "V3201" "V3202" "V3203" "V3204" "V3205" "V3206" "V3207" "V3208"
## [3209] "V3209" "V3210" "V3211" "V3212" "V3213" "V3214" "V3215" "V3216"
## [3217] "V3217" "V3218" "V3219" "V3220" "V3221" "V3222" "V3223" "V3224"
## [3225] "V3225" "V3226" "V3227" "V3228" "V3229" "V3230" "V3231" "V3232"
## [3233] "V3233" "V3234" "V3235" "V3236" "V3237" "V3238" "V3239" "V3240"
## [3241] "V3241" "V3242" "V3243" "V3244" "V3245" "V3246" "V3247" "V3248"
## [3249] "V3249" "V3250" "V3251" "V3252" "V3253" "V3254" "V3255" "V3256"
## [3257] "V3257" "V3258" "V3259" "V3260" "V3261" "V3262" "V3263" "V3264"
## [3265] "V3265" "V3266" "V3267" "V3268" "V3269" "V3270" "V3271" "V3272"
## [3273] "V3273" "V3274" "V3275" "V3276" "V3277" "V3278" "V3279" "V3280"
## [3281] "V3281" "V3282" "V3283" "V3284" "V3285" "V3286" "V3287" "V3288"
## [3289] "V3289" "V3290" "V3291" "V3292" "V3293" "V3294" "V3295" "V3296"
## [3297] "V3297" "V3298" "V3299" "V3300" "V3301" "V3302" "V3303" "V3304"
## [3305] "V3305" "V3306" "V3307" "V3308" "V3309" "V3310" "V3311" "V3312"
## [3313] "V3313" "V3314" "V3315" "V3316" "V3317" "V3318" "V3319" "V3320"
## [3321] "V3321" "V3322" "V3323" "V3324" "V3325" "V3326" "V3327" "V3328"
## [3329] "V3329" "V3330" "V3331" "V3332" "V3333" "V3334" "V3335" "V3336"
## [3337] "V3337" "V3338" "V3339" "V3340" "V3341" "V3342" "V3343" "V3344"
## [3345] "V3345" "V3346" "V3347" "V3348" "V3349" "V3350" "V3351" "V3352"
## [3353] "V3353" "V3354" "V3355" "V3356" "V3357" "V3358" "V3359" "V3360"
## [3361] "V3361" "V3362" "V3363" "V3364" "V3365" "V3366" "V3367" "V3368"
## [3369] "V3369" "V3370" "V3371" "V3372" "V3373" "V3374" "V3375" "V3376"
## [3377] "V3377" "V3378" "V3379" "V3380" "V3381" "V3382" "V3383" "V3384"
## [3385] "V3385" "V3386" "V3387" "V3388" "V3389" "V3390" "V3391" "V3392"
## [3393] "V3393" "V3394" "V3395" "V3396" "V3397" "V3398" "V3399" "V3400"
## [3401] "V3401" "V3402" "V3403" "V3404" "V3405" "V3406" "V3407" "V3408"
## [3409] "V3409" "V3410" "V3411" "V3412" "V3413" "V3414" "V3415" "V3416"
## [3417] "V3417" "V3418" "V3419" "V3420" "V3421" "V3422" "V3423" "V3424"
## [3425] "V3425" "V3426" "V3427" "V3428" "V3429" "V3430" "V3431" "V3432"
## [3433] "V3433" "V3434" "V3435" "V3436" "V3437" "V3438" "V3439" "V3440"
## [3441] "V3441" "V3442" "V3443" "V3444" "V3445" "V3446" "V3447" "V3448"
## [3449] "V3449" "V3450" "V3451" "V3452" "V3453" "V3454" "V3455" "V3456"
## [3457] "V3457" "V3458" "V3459" "V3460" "V3461" "V3462" "V3463" "V3464"
## [3465] "V3465" "V3466" "V3467" "V3468" "V3469" "V3470" "V3471" "V3472"
## [3473] "V3473" "V3474" "V3475" "V3476" "V3477" "V3478" "V3479" "V3480"
## [3481] "V3481" "V3482" "V3483" "V3484" "V3485" "V3486" "V3487" "V3488"
## [3489] "V3489" "V3490" "V3491" "V3492" "V3493" "V3494" "V3495" "V3496"
## [3497] "V3497" "V3498" "V3499" "V3500" "V3501" "V3502" "V3503" "V3504"
## [3505] "V3505" "V3506" "V3507" "V3508" "V3509" "V3510" "V3511" "V3512"
## [3513] "V3513" "V3514" "V3515" "V3516" "V3517" "V3518" "V3519" "V3520"
## [3521] "V3521" "V3522" "V3523" "V3524" "V3525" "V3526" "V3527" "V3528"
## [3529] "V3529" "V3530" "V3531" "V3532" "V3533" "V3534" "V3535" "V3536"
## [3537] "V3537" "V3538" "V3539" "V3540" "V3541" "V3542" "V3543" "V3544"
## [3545] "V3545" "V3546" "V3547" "V3548" "V3549" "V3550" "V3551" "V3552"
## [3553] "V3553" "V3554" "V3555" "V3556" "V3557" "V3558" "V3559" "V3560"
## [3561] "V3561" "V3562" "V3563" "V3564" "V3565" "V3566" "V3567" "V3568"
## [3569] "V3569" "V3570" "V3571" "V3572" "V3573" "V3574" "V3575" "V3576"
## [3577] "V3577" "V3578" "V3579" "V3580" "V3581" "V3582" "V3583" "V3584"
## [3585] "V3585" "V3586" "V3587" "V3588" "V3589" "V3590" "V3591" "V3592"
## [3593] "V3593" "V3594" "V3595" "V3596" "V3597" "V3598" "V3599" "V3600"
## [3601] "V3601" "V3602" "V3603" "V3604" "V3605" "V3606" "V3607" "V3608"
## [3609] "V3609" "V3610" "V3611" "V3612" "V3613" "V3614" "V3615" "V3616"
## [3617] "V3617" "V3618" "V3619" "V3620" "V3621" "V3622" "V3623" "V3624"
## [3625] "V3625" "V3626" "V3627" "V3628" "V3629" "V3630" "V3631" "V3632"
## [3633] "V3633" "V3634" "V3635" "V3636" "V3637" "V3638" "V3639" "V3640"
## [3641] "V3641" "V3642" "V3643" "V3644" "V3645" "V3646" "V3647" "V3648"
## [3649] "V3649" "V3650" "V3651" "V3652" "V3653" "V3654" "V3655" "V3656"
## [3657] "V3657" "V3658" "V3659" "V3660" "V3661" "V3662" "V3663" "V3664"
## [3665] "V3665" "V3666" "V3667" "V3668" "V3669" "V3670" "V3671" "V3672"
## [3673] "V3673" "V3674" "V3675" "V3676" "V3677" "V3678" "V3679" "V3680"
## [3681] "V3681" "V3682" "V3683" "V3684" "V3685" "V3686" "V3687" "V3688"
## [3689] "V3689" "V3690" "V3691" "V3692" "V3693" "V3694" "V3695" "V3696"
## [3697] "V3697" "V3698" "V3699" "V3700" "V3701" "V3702" "V3703" "V3704"
## [3705] "V3705" "V3706" "V3707" "V3708" "V3709" "V3710" "V3711" "V3712"
## [3713] "V3713" "V3714" "V3715" "V3716" "V3717" "V3718" "V3719" "V3720"
## [3721] "V3721" "V3722" "V3723" "V3724" "V3725" "V3726" "V3727" "V3728"
## [3729] "V3729" "V3730" "V3731" "V3732" "V3733" "V3734" "V3735" "V3736"
## [3737] "V3737" "V3738" "V3739" "V3740" "V3741" "V3742" "V3743" "V3744"
## [3745] "V3745" "V3746" "V3747" "V3748" "V3749" "V3750" "V3751" "V3752"
## [3753] "V3753" "V3754" "V3755" "V3756" "V3757" "V3758" "V3759" "V3760"
## [3761] "V3761" "V3762" "V3763" "V3764" "V3765" "V3766" "V3767" "V3768"
## [3769] "V3769" "V3770" "V3771" "V3772" "V3773" "V3774" "V3775" "V3776"
## [3777] "V3777" "V3778" "V3779" "V3780" "V3781" "V3782" "V3783" "V3784"
## [3785] "V3785" "V3786" "V3787" "V3788" "V3789" "V3790" "V3791" "V3792"
## [3793] "V3793" "V3794" "V3795" "V3796" "V3797" "V3798" "V3799" "V3800"
## [3801] "V3801" "V3802" "V3803" "V3804" "V3805" "V3806" "V3807" "V3808"
## [3809] "V3809" "V3810" "V3811" "V3812" "V3813" "V3814" "V3815" "V3816"
## [3817] "V3817" "V3818" "V3819" "V3820" "V3821" "V3822" "V3823" "V3824"
## [3825] "V3825" "V3826" "V3827" "V3828" "V3829" "V3830" "V3831" "V3832"
## [3833] "V3833" "V3834" "V3835" "V3836" "V3837" "V3838" "V3839" "V3840"
## [3841] "V3841" "V3842" "V3843" "V3844" "V3845" "V3846" "V3847" "V3848"
## [3849] "V3849" "V3850" "V3851" "V3852" "V3853" "V3854" "V3855" "V3856"
## [3857] "V3857" "V3858" "V3859" "V3860" "V3861" "V3862" "V3863" "V3864"
## [3865] "V3865" "V3866" "V3867" "V3868" "V3869" "V3870" "V3871" "V3872"
## [3873] "V3873" "V3874" "V3875" "V3876" "V3877" "V3878" "V3879" "V3880"
## [3881] "V3881" "V3882" "V3883" "V3884" "V3885" "V3886" "V3887" "V3888"
## [3889] "V3889" "V3890" "V3891" "V3892" "V3893" "V3894" "V3895" "V3896"
## [3897] "V3897" "V3898" "V3899" "V3900" "V3901" "V3902" "V3903" "V3904"
## [3905] "V3905" "V3906" "V3907" "V3908" "V3909" "V3910" "V3911" "V3912"
## [3913] "V3913" "V3914" "V3915" "V3916" "V3917" "V3918" "V3919" "V3920"
## [3921] "V3921" "V3922" "V3923" "V3924" "V3925" "V3926" "V3927" "V3928"
## [3929] "V3929" "V3930" "V3931" "V3932" "V3933" "V3934" "V3935" "V3936"
## [3937] "V3937" "V3938" "V3939" "V3940" "V3941" "V3942" "V3943" "V3944"
## [3945] "V3945" "V3946" "V3947" "V3948" "V3949" "V3950" "V3951" "V3952"
## [3953] "V3953" "V3954" "V3955" "V3956" "V3957" "V3958" "V3959" "V3960"
## [3961] "V3961" "V3962" "V3963" "V3964" "V3965" "V3966" "V3967" "V3968"
## [3969] "V3969" "V3970" "V3971" "V3972" "V3973" "V3974" "V3975" "V3976"
## [3977] "V3977" "V3978" "V3979" "V3980" "V3981" "V3982" "V3983" "V3984"
## [3985] "V3985" "V3986" "V3987" "V3988" "V3989" "V3990" "V3991" "V3992"
## [3993] "V3993" "V3994" "V3995" "V3996" "V3997" "V3998" "V3999" "V4000"
## [4001] "V4001" "V4002" "V4003" "V4004" "V4005" "V4006" "V4007" "V4008"
## [4009] "V4009" "V4010" "V4011" "V4012" "V4013" "V4014" "V4015" "V4016"
## [4017] "V4017" "V4018" "V4019" "V4020" "V4021" "V4022" "V4023" "V4024"
## [4025] "V4025" "V4026" "V4027" "V4028" "V4029" "V4030" "V4031" "V4032"
## [4033] "V4033" "V4034" "V4035" "V4036" "V4037" "V4038" "V4039" "V4040"
## [4041] "V4041" "V4042" "V4043" "V4044" "V4045" "V4046" "V4047" "V4048"
## [4049] "V4049" "V4050" "V4051" "V4052" "V4053" "V4054" "V4055" "V4056"
## [4057] "V4057" "V4058" "V4059" "V4060" "V4061" "V4062" "V4063" "V4064"
## [4065] "V4065" "V4066" "V4067" "V4068" "V4069" "V4070" "V4071" "V4072"
## [4073] "V4073" "V4074" "V4075" "V4076" "V4077" "V4078" "V4079" "V4080"
## [4081] "V4081" "V4082" "V4083" "V4084" "V4085" "V4086" "V4087" "V4088"
## [4089] "V4089" "V4090" "V4091" "V4092" "V4093" "V4094" "V4095" "V4096"
## [4097] "V4097" "V4098" "V4099" "V4100" "V4101" "V4102" "V4103" "V4104"
## [4105] "V4105" "V4106" "V4107" "V4108" "V4109" "V4110" "V4111" "V4112"
## [4113] "V4113" "V4114" "V4115" "V4116" "V4117" "V4118" "V4119" "V4120"
## [4121] "V4121" "V4122" "V4123" "V4124" "V4125" "V4126" "V4127" "V4128"
## [4129] "V4129" "V4130" "V4131" "V4132" "V4133" "V4134" "V4135" "V4136"
## [4137] "V4137" "V4138" "V4139" "V4140" "V4141" "V4142" "V4143" "V4144"
## [4145] "V4145" "V4146" "V4147" "V4148" "V4149" "V4150" "V4151" "V4152"
## [4153] "V4153" "V4154" "V4155" "V4156" "V4157" "V4158" "V4159" "V4160"
## [4161] "V4161" "V4162" "V4163" "V4164" "V4165" "V4166" "V4167" "V4168"
## [4169] "V4169" "V4170" "V4171" "V4172" "V4173" "V4174" "V4175" "V4176"
## [4177] "V4177" "V4178" "V4179" "V4180" "V4181" "V4182" "V4183" "V4184"
## [4185] "V4185" "V4186" "V4187" "V4188" "V4189" "V4190" "V4191" "V4192"
## [4193] "V4193" "V4194" "V4195" "V4196" "V4197" "V4198" "V4199" "V4200"
## [4201] "V4201" "V4202" "V4203" "V4204" "V4205" "V4206" "V4207" "V4208"
## [4209] "V4209" "V4210" "V4211" "V4212" "V4213" "V4214" "V4215" "V4216"
## [4217] "V4217" "V4218" "V4219" "V4220" "V4221" "V4222" "V4223" "V4224"
## [4225] "V4225" "V4226" "V4227" "V4228" "V4229" "V4230" "V4231" "V4232"
## [4233] "V4233" "V4234" "V4235" "V4236" "V4237" "V4238" "V4239" "V4240"
## [4241] "V4241" "V4242" "V4243" "V4244" "V4245" "V4246" "V4247" "V4248"
## [4249] "V4249" "V4250" "V4251" "V4252" "V4253" "V4254" "V4255" "V4256"
## [4257] "V4257" "V4258" "V4259" "V4260" "V4261" "V4262" "V4263" "V4264"
## [4265] "V4265" "V4266" "V4267" "V4268" "V4269" "V4270" "V4271" "V4272"
## [4273] "V4273" "V4274" "V4275" "V4276" "V4277" "V4278" "V4279" "V4280"
## [4281] "V4281" "V4282" "V4283" "V4284" "V4285" "V4286" "V4287" "V4288"
## [4289] "V4289" "V4290" "V4291" "V4292" "V4293" "V4294" "V4295" "V4296"
## [4297] "V4297" "V4298" "V4299" "V4300" "V4301" "V4302" "V4303" "V4304"
## [4305] "V4305" "V4306" "V4307" "V4308" "V4309" "V4310" "V4311" "V4312"
## [4313] "V4313" "V4314" "V4315" "V4316" "V4317" "V4318" "V4319" "V4320"
## [4321] "V4321" "V4322" "V4323" "V4324" "V4325" "V4326" "V4327" "V4328"
## [4329] "V4329" "V4330" "V4331" "V4332" "V4333" "V4334" "V4335" "V4336"
## [4337] "V4337" "V4338" "V4339" "V4340" "V4341" "V4342" "V4343" "V4344"
## [4345] "V4345" "V4346" "V4347" "V4348" "V4349" "V4350" "V4351" "V4352"
## [4353] "V4353" "V4354" "V4355" "V4356" "V4357" "V4358" "V4359" "V4360"
## [4361] "V4361" "V4362" "V4363" "V4364" "V4365" "V4366" "V4367" "V4368"
## [4369] "V4369" "V4370" "V4371" "V4372" "V4373" "V4374" "V4375" "V4376"
## [4377] "V4377" "V4378" "V4379" "V4380" "V4381" "V4382" "V4383" "V4384"
## [4385] "V4385" "V4386" "V4387" "V4388" "V4389" "V4390" "V4391" "V4392"
## [4393] "V4393" "V4394" "V4395" "V4396" "V4397" "V4398" "V4399" "V4400"
## [4401] "V4401" "V4402" "V4403" "V4404" "V4405" "V4406" "V4407" "V4408"
## [4409] "V4409" "V4410" "V4411" "V4412" "V4413" "V4414" "V4415" "V4416"
## [4417] "V4417" "V4418" "V4419" "V4420" "V4421" "V4422" "V4423" "V4424"
## [4425] "V4425" "V4426" "V4427" "V4428" "V4429" "V4430" "V4431" "V4432"
## [4433] "V4433" "V4434" "V4435" "V4436" "V4437" "V4438" "V4439" "V4440"
## [4441] "V4441" "V4442" "V4443" "V4444" "V4445" "V4446" "V4447" "V4448"
## [4449] "V4449" "V4450" "V4451" "V4452" "V4453" "V4454" "V4455" "V4456"
## [4457] "V4457" "V4458" "V4459" "V4460" "V4461" "V4462" "V4463" "V4464"
## [4465] "V4465" "V4466" "V4467" "V4468" "V4469" "V4470" "V4471" "V4472"
## [4473] "V4473" "V4474" "V4475" "V4476" "V4477" "V4478" "V4479" "V4480"
## [4481] "V4481" "V4482" "V4483" "V4484" "V4485" "V4486" "V4487" "V4488"
## [4489] "V4489" "V4490" "V4491" "V4492" "V4493" "V4494" "V4495" "V4496"
## [4497] "V4497" "V4498" "V4499" "V4500" "V4501" "V4502" "V4503" "V4504"
## [4505] "V4505" "V4506" "V4507" "V4508" "V4509" "V4510" "V4511" "V4512"
## [4513] "V4513" "V4514" "V4515" "V4516" "V4517" "V4518" "V4519" "V4520"
## [4521] "V4521" "V4522" "V4523" "V4524" "V4525" "V4526" "V4527" "V4528"
## [4529] "V4529" "V4530" "V4531" "V4532" "V4533" "V4534" "V4535" "V4536"
## [4537] "V4537" "V4538" "V4539" "V4540" "V4541" "V4542" "V4543" "V4544"
## [4545] "V4545" "V4546" "V4547" "V4548" "V4549" "V4550" "V4551" "V4552"
## [4553] "V4553" "V4554" "V4555" "V4556" "V4557" "V4558" "V4559" "V4560"
## [4561] "V4561" "V4562" "V4563" "V4564" "V4565" "V4566" "V4567" "V4568"
## [4569] "V4569" "V4570" "V4571" "V4572" "V4573" "V4574" "V4575" "V4576"
## [4577] "V4577" "V4578" "V4579" "V4580" "V4581" "V4582" "V4583" "V4584"
## [4585] "V4585" "V4586" "V4587" "V4588" "V4589" "V4590" "V4591" "V4592"
## [4593] "V4593" "V4594" "V4595" "V4596" "V4597" "V4598" "V4599" "V4600"
## [4601] "V4601" "V4602" "V4603" "V4604" "V4605" "V4606" "V4607" "V4608"
## [4609] "V4609" "V4610" "V4611" "V4612" "V4613" "V4614" "V4615" "V4616"
## [4617] "V4617" "V4618" "V4619" "V4620" "V4621" "V4622" "V4623" "V4624"
## [4625] "V4625" "V4626" "V4627" "V4628" "V4629" "V4630" "V4631" "V4632"
## [4633] "V4633" "V4634" "V4635" "V4636" "V4637" "V4638" "V4639" "V4640"
## [4641] "V4641" "V4642" "V4643" "V4644" "V4645" "V4646" "V4647" "V4648"
## [4649] "V4649" "V4650" "V4651" "V4652" "V4653" "V4654" "V4655" "V4656"
## [4657] "V4657" "V4658" "V4659" "V4660" "V4661" "V4662" "V4663" "V4664"
## [4665] "V4665" "V4666" "V4667" "V4668" "V4669" "V4670" "V4671" "V4672"
## [4673] "V4673" "V4674" "V4675" "V4676" "V4677" "V4678" "V4679" "V4680"
## [4681] "V4681" "V4682" "V4683" "V4684" "V4685" "V4686" "V4687" "V4688"
## [4689] "V4689" "V4690" "V4691" "V4692" "V4693" "V4694" "V4695" "V4696"
## [4697] "V4697" "V4698" "V4699" "V4700" "V4701" "V4702" "V4703" "V4704"
## [4705] "V4705" "V4706" "V4707" "V4708" "V4709" "V4710" "V4711" "V4712"
## [4713] "V4713" "V4714" "V4715" "V4716" "V4717" "V4718" "V4719" "V4720"
## [4721] "V4721" "V4722" "V4723" "V4724" "V4725" "V4726" "V4727" "V4728"
## [4729] "V4729" "V4730" "V4731" "V4732" "V4733" "V4734" "V4735" "V4736"
## [4737] "V4737" "V4738" "V4739" "V4740" "V4741" "V4742" "V4743" "V4744"
## [4745] "V4745" "V4746" "V4747" "V4748" "V4749" "V4750" "V4751" "V4752"
## [4753] "V4753" "V4754" "V4755" "V4756" "V4757" "V4758" "V4759" "V4760"
## [4761] "V4761" "V4762" "V4763" "V4764" "V4765" "V4766" "V4767" "V4768"
## [4769] "V4769" "V4770" "V4771" "V4772" "V4773" "V4774" "V4775" "V4776"
## [4777] "V4777" "V4778" "V4779" "V4780" "V4781" "V4782" "V4783" "V4784"
## [4785] "V4785" "V4786" "V4787" "V4788" "V4789" "V4790" "V4791" "V4792"
## [4793] "V4793" "V4794" "V4795" "V4796" "V4797" "V4798" "V4799" "V4800"
## [4801] "V4801" "V4802" "V4803" "V4804" "V4805" "V4806" "V4807" "V4808"
## [4809] "V4809" "V4810" "V4811" "V4812" "V4813" "V4814" "V4815" "V4816"
## [4817] "V4817" "V4818" "V4819" "V4820" "V4821" "V4822" "V4823" "V4824"
## [4825] "V4825" "V4826" "V4827" "V4828" "V4829" "V4830" "V4831" "V4832"
## [4833] "V4833" "V4834" "V4835" "V4836" "V4837" "V4838" "V4839" "V4840"
## [4841] "V4841" "V4842" "V4843" "V4844" "V4845" "V4846" "V4847" "V4848"
## [4849] "V4849" "V4850" "V4851" "V4852" "V4853" "V4854" "V4855" "V4856"
## [4857] "V4857" "V4858" "V4859" "V4860" "V4861" "V4862" "V4863" "V4864"
## [4865] "V4865" "V4866" "V4867" "V4868" "V4869" "V4870" "V4871" "V4872"
## [4873] "V4873" "V4874" "V4875" "V4876" "V4877" "V4878" "V4879" "V4880"
## [4881] "V4881" "V4882" "V4883" "V4884" "V4885" "V4886" "V4887" "V4888"
## [4889] "V4889" "V4890" "V4891" "V4892" "V4893" "V4894" "V4895" "V4896"
## [4897] "V4897" "V4898" "V4899" "V4900" "V4901" "V4902" "V4903" "V4904"
## [4905] "V4905" "V4906" "V4907" "V4908" "V4909" "V4910" "V4911" "V4912"
## [4913] "V4913" "V4914" "V4915" "V4916" "V4917" "V4918" "V4919" "V4920"
## [4921] "V4921" "V4922" "V4923" "V4924" "V4925" "V4926" "V4927" "V4928"
## [4929] "V4929" "V4930" "V4931" "V4932" "V4933" "V4934" "V4935" "V4936"
## [4937] "V4937" "V4938" "V4939" "V4940" "V4941" "V4942" "V4943" "V4944"
## [4945] "V4945" "V4946" "V4947" "V4948" "V4949" "V4950" "V4951" "V4952"
## [4953] "V4953" "V4954" "V4955" "V4956" "V4957" "V4958" "V4959" "V4960"
## [4961] "V4961" "V4962" "V4963" "V4964" "V4965" "V4966" "V4967" "V4968"
## [4969] "V4969" "V4970" "V4971" "V4972" "V4973" "V4974" "V4975" "V4976"
## [4977] "V4977" "V4978" "V4979" "V4980" "V4981" "V4982" "V4983" "V4984"
## [4985] "V4985" "V4986" "V4987" "V4988" "V4989" "V4990" "V4991" "V4992"
## [4993] "V4993" "V4994" "V4995" "V4996" "V4997" "V4998" "V4999" "V5000"
## [5001] "V5001" "V5002" "V5003" "V5004" "V5005" "V5006" "V5007" "V5008"
## [5009] "V5009" "V5010" "V5011" "V5012" "V5013" "V5014" "V5015" "V5016"
## [5017] "V5017" "V5018" "V5019" "V5020" "V5021" "V5022" "V5023" "V5024"
## [5025] "V5025" "V5026" "V5027" "V5028" "V5029" "V5030" "V5031" "V5032"
## [5033] "V5033" "V5034" "V5035" "V5036" "V5037" "V5038" "V5039" "V5040"
## [5041] "V5041" "V5042" "V5043" "V5044" "V5045" "V5046" "V5047" "V5048"
## [5049] "V5049" "V5050" "V5051" "V5052" "V5053" "V5054" "V5055" "V5056"
## [5057] "V5057" "V5058" "V5059" "V5060" "V5061" "V5062" "V5063" "V5064"
## [5065] "V5065" "V5066" "V5067" "V5068" "V5069" "V5070" "V5071" "V5072"
## [5073] "V5073" "V5074" "V5075" "V5076" "V5077" "V5078" "V5079" "V5080"
## [5081] "V5081" "V5082" "V5083" "V5084" "V5085" "V5086" "V5087" "V5088"
## [5089] "V5089" "V5090" "V5091" "V5092" "V5093" "V5094" "V5095" "V5096"
## [5097] "V5097" "V5098" "V5099" "V5100" "V5101" "V5102" "V5103" "V5104"
## [5105] "V5105" "V5106" "V5107" "V5108" "V5109" "V5110" "V5111" "V5112"
## [5113] "V5113" "V5114" "V5115" "V5116" "V5117" "V5118" "V5119" "V5120"
## [5121] "V5121" "V5122" "V5123" "V5124" "V5125" "V5126" "V5127" "V5128"
## [5129] "V5129" "V5130" "V5131" "V5132" "V5133" "V5134" "V5135" "V5136"
## [5137] "V5137" "V5138" "V5139" "V5140" "V5141" "V5142" "V5143" "V5144"
## [5145] "V5145" "V5146" "V5147" "V5148" "V5149" "V5150" "V5151" "V5152"
## [5153] "V5153" "V5154" "V5155" "V5156" "V5157" "V5158" "V5159" "V5160"
## [5161] "V5161" "V5162" "V5163" "V5164" "V5165" "V5166" "V5167" "V5168"
## [5169] "V5169" "V5170" "V5171" "V5172" "V5173" "V5174" "V5175" "V5176"
## [5177] "V5177" "V5178" "V5179" "V5180" "V5181" "V5182" "V5183" "V5184"
## [5185] "V5185" "V5186" "V5187" "V5188" "V5189" "V5190" "V5191" "V5192"
## [5193] "V5193" "V5194" "V5195" "V5196" "V5197" "V5198" "V5199" "V5200"
## [5201] "V5201" "V5202" "V5203" "V5204" "V5205" "V5206" "V5207" "V5208"
## [5209] "V5209" "V5210" "V5211" "V5212" "V5213" "V5214" "V5215" "V5216"
## [5217] "V5217" "V5218" "V5219" "V5220" "V5221" "V5222" "V5223" "V5224"
## [5225] "V5225" "V5226" "V5227" "V5228" "V5229" "V5230" "V5231" "V5232"
## [5233] "V5233" "V5234" "V5235" "V5236" "V5237" "V5238" "V5239" "V5240"
## [5241] "V5241" "V5242" "V5243" "V5244" "V5245" "V5246" "V5247" "V5248"
## [5249] "V5249" "V5250" "V5251" "V5252" "V5253" "V5254" "V5255" "V5256"
## [5257] "V5257" "V5258" "V5259" "V5260" "V5261" "V5262" "V5263" "V5264"
## [5265] "V5265" "V5266" "V5267" "V5268" "V5269" "V5270" "V5271" "V5272"
## [5273] "V5273" "V5274" "V5275" "V5276" "V5277" "V5278" "V5279" "V5280"
## [5281] "V5281" "V5282" "V5283" "V5284" "V5285" "V5286" "V5287" "V5288"
## [5289] "V5289" "V5290" "V5291" "V5292" "V5293" "V5294" "V5295" "V5296"
## [5297] "V5297" "V5298" "V5299" "V5300" "V5301" "V5302" "V5303" "V5304"
## [5305] "V5305" "V5306" "V5307" "V5308" "V5309" "V5310" "V5311" "V5312"
## [5313] "V5313" "V5314" "V5315" "V5316" "V5317" "V5318" "V5319" "V5320"
## [5321] "V5321" "V5322" "V5323" "V5324" "V5325" "V5326" "V5327" "V5328"
## [5329] "V5329" "V5330" "V5331" "V5332" "V5333" "V5334" "V5335" "V5336"
## [5337] "V5337" "V5338" "V5339" "V5340" "V5341" "V5342" "V5343" "V5344"
## [5345] "V5345" "V5346" "V5347" "V5348" "V5349" "V5350" "V5351" "V5352"
## [5353] "V5353" "V5354" "V5355" "V5356" "V5357" "V5358" "V5359" "V5360"
## [5361] "V5361" "V5362" "V5363" "V5364" "V5365" "V5366" "V5367" "V5368"
## [5369] "V5369" "V5370" "V5371" "V5372" "V5373" "V5374" "V5375" "V5376"
## [5377] "V5377" "V5378" "V5379" "V5380" "V5381" "V5382" "V5383" "V5384"
## [5385] "V5385" "V5386" "V5387" "V5388" "V5389" "V5390" "V5391" "V5392"
## [5393] "V5393" "V5394" "V5395" "V5396" "V5397" "V5398" "V5399" "V5400"
## [5401] "V5401" "V5402" "V5403" "V5404" "V5405" "V5406" "V5407" "V5408"
## [5409] "V5409" "V5410" "V5411" "V5412" "V5413" "V5414" "V5415" "V5416"
## [5417] "V5417" "V5418" "V5419" "V5420" "V5421" "V5422" "V5423" "V5424"
## [5425] "V5425" "V5426" "V5427" "V5428" "V5429" "V5430" "V5431" "V5432"
## [5433] "V5433" "V5434" "V5435" "V5436" "V5437" "V5438" "V5439" "V5440"
## [5441] "V5441" "V5442" "V5443" "V5444" "V5445" "V5446" "V5447" "V5448"
## [5449] "V5449" "V5450" "V5451" "V5452" "V5453" "V5454" "V5455" "V5456"
## [5457] "V5457" "V5458" "V5459" "V5460" "V5461" "V5462" "V5463" "V5464"
## [5465] "V5465" "V5466" "V5467" "V5468" "V5469" "V5470" "V5471" "V5472"
## [5473] "V5473" "V5474" "V5475" "V5476" "V5477" "V5478" "V5479" "V5480"
## [5481] "V5481" "V5482" "V5483" "V5484" "V5485" "V5486" "V5487" "V5488"
## [5489] "V5489" "V5490" "V5491" "V5492" "V5493" "V5494" "V5495" "V5496"
## [5497] "V5497" "V5498" "V5499" "V5500" "V5501" "V5502" "V5503" "V5504"
## [5505] "V5505" "V5506" "V5507" "V5508" "V5509" "V5510" "V5511" "V5512"
## [5513] "V5513" "V5514" "V5515" "V5516" "V5517" "V5518" "V5519" "V5520"
## [5521] "V5521" "V5522" "V5523" "V5524" "V5525" "V5526" "V5527" "V5528"
## [5529] "V5529" "V5530" "V5531" "V5532" "V5533" "V5534" "V5535" "V5536"
## [5537] "V5537" "V5538" "V5539" "V5540" "V5541" "V5542" "V5543" "V5544"
## [5545] "V5545" "V5546" "V5547" "V5548" "V5549" "V5550" "V5551" "V5552"
## [5553] "V5553" "V5554" "V5555" "V5556" "V5557" "V5558" "V5559" "V5560"
## [5561] "V5561" "V5562" "V5563" "V5564" "V5565" "V5566" "V5567" "V5568"
## [5569] "V5569" "V5570" "V5571" "V5572" "V5573" "V5574" "V5575" "V5576"
## [5577] "V5577" "V5578" "V5579" "V5580" "V5581" "V5582" "V5583" "V5584"
## [5585] "V5585" "V5586" "V5587" "V5588" "V5589" "V5590" "V5591" "V5592"
## [5593] "V5593" "V5594" "V5595" "V5596" "V5597" "V5598" "V5599" "V5600"
## [5601] "V5601" "V5602" "V5603" "V5604" "V5605" "V5606" "V5607" "V5608"
## [5609] "V5609" "V5610" "V5611" "V5612" "V5613" "V5614" "V5615" "V5616"
## [5617] "V5617" "V5618" "V5619" "V5620" "V5621" "V5622" "V5623" "V5624"
## [5625] "V5625" "V5626" "V5627" "V5628" "V5629" "V5630" "V5631" "V5632"
## [5633] "V5633" "V5634" "V5635" "V5636" "V5637" "V5638" "V5639" "V5640"
## [5641] "V5641" "V5642" "V5643" "V5644" "V5645" "V5646" "V5647" "V5648"
## [5649] "V5649" "V5650" "V5651" "V5652" "V5653" "V5654" "V5655" "V5656"
## [5657] "V5657" "V5658" "V5659" "V5660" "V5661" "V5662" "V5663" "V5664"
## [5665] "V5665" "V5666" "V5667" "V5668" "V5669" "V5670" "V5671" "V5672"
## [5673] "V5673" "V5674" "V5675" "V5676" "V5677" "V5678" "V5679" "V5680"
## [5681] "V5681" "V5682" "V5683" "V5684" "V5685" "V5686" "V5687" "V5688"
## [5689] "V5689" "V5690" "V5691" "V5692" "V5693" "V5694" "V5695" "V5696"
## [5697] "V5697" "V5698" "V5699" "V5700" "V5701" "V5702" "V5703" "V5704"
## [5705] "V5705" "V5706" "V5707" "V5708" "V5709" "V5710" "V5711" "V5712"
## [5713] "V5713" "V5714" "V5715" "V5716" "V5717" "V5718" "V5719" "V5720"
## [5721] "V5721" "V5722" "V5723" "V5724" "V5725" "V5726" "V5727" "V5728"
## [5729] "V5729" "V5730" "V5731" "V5732" "V5733" "V5734" "V5735" "V5736"
## [5737] "V5737" "V5738" "V5739" "V5740" "V5741" "V5742" "V5743" "V5744"
## [5745] "V5745" "V5746" "V5747" "V5748" "V5749" "V5750" "V5751" "V5752"
## [5753] "V5753" "V5754" "V5755" "V5756" "V5757" "V5758" "V5759" "V5760"
## [5761] "V5761" "V5762" "V5763" "V5764" "V5765" "V5766" "V5767" "V5768"
## [5769] "V5769" "V5770" "V5771" "V5772" "V5773" "V5774" "V5775" "V5776"
## [5777] "V5777" "V5778" "V5779" "V5780" "V5781" "V5782" "V5783" "V5784"
## [5785] "V5785" "V5786" "V5787" "V5788" "V5789" "V5790" "V5791" "V5792"
## [5793] "V5793" "V5794" "V5795" "V5796" "V5797" "V5798" "V5799" "V5800"
## [5801] "V5801" "V5802" "V5803" "V5804" "V5805" "V5806" "V5807" "V5808"
## [5809] "V5809" "V5810" "V5811" "V5812" "V5813" "V5814" "V5815" "V5816"
## [5817] "V5817" "V5818" "V5819" "V5820" "V5821" "V5822" "V5823" "V5824"
## [5825] "V5825" "V5826" "V5827" "V5828" "V5829" "V5830" "V5831" "V5832"
## [5833] "V5833" "V5834" "V5835" "V5836" "V5837" "V5838" "V5839" "V5840"
## [5841] "V5841" "V5842" "V5843" "V5844" "V5845" "V5846" "V5847" "V5848"
## [5849] "V5849" "V5850" "V5851" "V5852" "V5853" "V5854" "V5855" "V5856"
## [5857] "V5857" "V5858" "V5859" "V5860" "V5861" "V5862" "V5863" "V5864"
## [5865] "V5865" "V5866" "V5867" "V5868" "V5869" "V5870" "V5871" "V5872"
## [5873] "V5873" "V5874" "V5875" "V5876" "V5877" "V5878" "V5879" "V5880"
## [5881] "V5881" "V5882" "V5883" "V5884" "V5885" "V5886" "V5887" "V5888"
## [5889] "V5889" "V5890" "V5891" "V5892" "V5893" "V5894" "V5895" "V5896"
## [5897] "V5897" "V5898" "V5899" "V5900" "V5901" "V5902" "V5903" "V5904"
## [5905] "V5905" "V5906" "V5907" "V5908" "V5909" "V5910" "V5911" "V5912"
## [5913] "V5913" "V5914" "V5915" "V5916" "V5917" "V5918" "V5919" "V5920"
## [5921] "V5921" "V5922" "V5923" "V5924" "V5925" "V5926" "V5927" "V5928"
## [5929] "V5929" "V5930" "V5931" "V5932" "V5933" "V5934" "V5935" "V5936"
## [5937] "V5937" "V5938" "V5939" "V5940" "V5941" "V5942" "V5943" "V5944"
## [5945] "V5945" "V5946" "V5947" "V5948" "V5949" "V5950" "V5951" "V5952"
## [5953] "V5953" "V5954" "V5955" "V5956" "V5957" "V5958" "V5959" "V5960"
## [5961] "V5961" "V5962" "V5963" "V5964" "V5965" "V5966" "V5967" "V5968"
## [5969] "V5969" "V5970" "V5971" "V5972" "V5973" "V5974" "V5975" "V5976"
## [5977] "V5977" "V5978" "V5979" "V5980" "V5981" "V5982" "V5983" "V5984"
## [5985] "V5985" "V5986" "V5987" "V5988" "V5989" "V5990" "V5991" "V5992"
## [5993] "V5993" "V5994" "V5995" "V5996" "V5997" "V5998" "V5999" "V6000"
## [6001] "V6001" "V6002" "V6003" "V6004" "V6005" "V6006" "V6007" "V6008"
## [6009] "V6009" "V6010" "V6011" "V6012" "V6013" "V6014" "V6015" "V6016"
## [6017] "V6017" "V6018" "V6019" "V6020" "V6021" "V6022" "V6023" "V6024"
## [6025] "V6025" "V6026" "V6027" "V6028" "V6029" "V6030" "V6031" "V6032"
## [6033] "V6033" "V6034" "V6035" "V6036" "V6037" "V6038" "V6039" "V6040"
## [6041] "V6041" "V6042" "V6043" "V6044" "V6045" "V6046" "V6047" "V6048"
## [6049] "V6049" "V6050" "V6051" "V6052" "V6053" "V6054" "V6055" "V6056"
## [6057] "V6057" "V6058" "V6059" "V6060" "V6061" "V6062" "V6063" "V6064"
## [6065] "V6065" "V6066" "V6067" "V6068" "V6069" "V6070" "V6071" "V6072"
## [6073] "V6073" "V6074" "V6075" "V6076" "V6077" "V6078" "V6079" "V6080"
## [6081] "V6081" "V6082" "V6083" "V6084" "V6085" "V6086" "V6087" "V6088"
## [6089] "V6089" "V6090" "V6091" "V6092" "V6093" "V6094" "V6095" "V6096"
## [6097] "V6097" "V6098" "V6099" "V6100" "V6101" "V6102" "V6103" "V6104"
## [6105] "V6105" "V6106" "V6107" "V6108" "V6109" "V6110" "V6111" "V6112"
## [6113] "V6113" "V6114" "V6115" "V6116" "V6117" "V6118" "V6119" "V6120"
## [6121] "V6121" "V6122" "V6123" "V6124" "V6125" "V6126" "V6127" "V6128"
## [6129] "V6129" "V6130" "V6131" "V6132" "V6133" "V6134" "V6135" "V6136"
## [6137] "V6137" "V6138" "V6139" "V6140" "V6141" "V6142" "V6143" "V6144"
## [6145] "V6145" "V6146" "V6147" "V6148" "V6149" "V6150" "V6151" "V6152"
## [6153] "V6153" "V6154" "V6155" "V6156" "V6157" "V6158" "V6159" "V6160"
## [6161] "V6161" "V6162" "V6163" "V6164" "V6165" "V6166" "V6167" "V6168"
## [6169] "V6169" "V6170" "V6171" "V6172" "V6173" "V6174" "V6175" "V6176"
## [6177] "V6177" "V6178" "V6179" "V6180" "V6181" "V6182" "V6183" "V6184"
## [6185] "V6185" "V6186" "V6187" "V6188" "V6189" "V6190" "V6191" "V6192"
## [6193] "V6193" "V6194" "V6195" "V6196" "V6197" "V6198" "V6199" "V6200"
## [6201] "V6201" "V6202" "V6203" "V6204" "V6205" "V6206" "V6207" "V6208"
## [6209] "V6209" "V6210" "V6211" "V6212" "V6213" "V6214" "V6215" "V6216"
## [6217] "V6217" "V6218" "V6219" "V6220" "V6221" "V6222" "V6223" "V6224"
## [6225] "V6225" "V6226" "V6227" "V6228" "V6229" "V6230" "V6231" "V6232"
## [6233] "V6233" "V6234" "V6235" "V6236" "V6237" "V6238" "V6239" "V6240"
## [6241] "V6241" "V6242" "V6243" "V6244" "V6245" "V6246" "V6247" "V6248"
## [6249] "V6249" "V6250" "V6251" "V6252" "V6253" "V6254" "V6255" "V6256"
## [6257] "V6257" "V6258" "V6259" "V6260" "V6261" "V6262" "V6263" "V6264"
## [6265] "V6265" "V6266" "V6267" "V6268" "V6269" "V6270" "V6271" "V6272"
## [6273] "V6273" "V6274" "V6275" "V6276" "V6277" "V6278" "V6279" "V6280"
## [6281] "V6281" "V6282" "V6283" "V6284" "V6285" "V6286" "V6287" "V6288"
## [6289] "V6289" "V6290" "V6291" "V6292" "V6293" "V6294" "V6295" "V6296"
## [6297] "V6297" "V6298" "V6299" "V6300" "V6301" "V6302" "V6303" "V6304"
## [6305] "V6305" "V6306" "V6307" "V6308" "V6309" "V6310" "V6311" "V6312"
## [6313] "V6313" "V6314" "V6315" "V6316" "V6317" "V6318" "V6319" "V6320"
## [6321] "V6321" "V6322" "V6323" "V6324" "V6325" "V6326" "V6327" "V6328"
## [6329] "V6329" "V6330" "V6331" "V6332" "V6333" "V6334" "V6335" "V6336"
## [6337] "V6337" "V6338" "V6339" "V6340" "V6341" "V6342" "V6343" "V6344"
## [6345] "V6345" "V6346" "V6347" "V6348" "V6349" "V6350" "V6351" "V6352"
## [6353] "V6353" "V6354" "V6355" "V6356" "V6357" "V6358" "V6359" "V6360"
## [6361] "V6361" "V6362" "V6363" "V6364" "V6365" "V6366" "V6367" "V6368"
## [6369] "V6369" "V6370" "V6371" "V6372" "V6373" "V6374" "V6375" "V6376"
## [6377] "V6377" "V6378" "V6379" "V6380" "V6381" "V6382" "V6383" "V6384"
## [6385] "V6385" "V6386" "V6387" "V6388" "V6389" "V6390" "V6391" "V6392"
## [6393] "V6393" "V6394" "V6395" "V6396" "V6397" "V6398" "V6399" "V6400"
## [6401] "V6401" "V6402" "V6403" "V6404" "V6405" "V6406" "V6407" "V6408"
## [6409] "V6409" "V6410" "V6411" "V6412" "V6413" "V6414" "V6415" "V6416"
## [6417] "V6417" "V6418" "V6419" "V6420" "V6421" "V6422" "V6423" "V6424"
## [6425] "V6425" "V6426" "V6427" "V6428" "V6429" "V6430" "V6431" "V6432"
## [6433] "V6433" "V6434" "V6435" "V6436" "V6437" "V6438" "V6439" "V6440"
## [6441] "V6441" "V6442" "V6443" "V6444" "V6445" "V6446" "V6447" "V6448"
## [6449] "V6449" "V6450" "V6451" "V6452" "V6453" "V6454" "V6455" "V6456"
## [6457] "V6457" "V6458" "V6459" "V6460" "V6461" "V6462" "V6463" "V6464"
## [6465] "V6465" "V6466" "V6467" "V6468" "V6469" "V6470" "V6471" "V6472"
## [6473] "V6473" "V6474" "V6475" "V6476" "V6477" "V6478" "V6479" "V6480"
## [6481] "V6481" "V6482" "V6483" "V6484" "V6485" "V6486" "V6487" "V6488"
## [6489] "V6489" "V6490" "V6491" "V6492" "V6493" "V6494" "V6495" "V6496"
## [6497] "V6497" "V6498" "V6499" "V6500" "V6501" "V6502" "V6503" "V6504"
## [6505] "V6505" "V6506" "V6507" "V6508" "V6509" "V6510" "V6511" "V6512"
## [6513] "V6513" "V6514" "V6515" "V6516" "V6517" "V6518" "V6519" "V6520"
## [6521] "V6521" "V6522" "V6523" "V6524" "V6525" "V6526" "V6527" "V6528"
## [6529] "V6529" "V6530" "V6531" "V6532" "V6533" "V6534" "V6535" "V6536"
## [6537] "V6537" "V6538" "V6539" "V6540" "V6541" "V6542" "V6543" "V6544"
## [6545] "V6545" "V6546" "V6547" "V6548" "V6549" "V6550" "V6551" "V6552"
## [6553] "V6553" "V6554" "V6555" "V6556" "V6557" "V6558" "V6559" "V6560"
## [6561] "V6561" "V6562" "V6563" "V6564" "V6565" "V6566" "V6567" "V6568"
## [6569] "V6569" "V6570" "V6571" "V6572" "V6573" "V6574" "V6575" "V6576"
## [6577] "V6577" "V6578" "V6579" "V6580" "V6581" "V6582" "V6583" "V6584"
## [6585] "V6585" "V6586" "V6587" "V6588" "V6589" "V6590" "V6591" "V6592"
## [6593] "V6593" "V6594" "V6595" "V6596" "V6597" "V6598" "V6599" "V6600"
## [6601] "V6601" "V6602" "V6603" "V6604" "V6605" "V6606" "V6607" "V6608"
## [6609] "V6609" "V6610" "V6611" "V6612" "V6613" "V6614" "V6615" "V6616"
## [6617] "V6617" "V6618" "V6619" "V6620" "V6621" "V6622" "V6623" "V6624"
## [6625] "V6625" "V6626" "V6627" "V6628" "V6629" "V6630" "V6631" "V6632"
## [6633] "V6633" "V6634" "V6635" "V6636" "V6637" "V6638" "V6639" "V6640"
## [6641] "V6641" "V6642" "V6643" "V6644" "V6645" "V6646" "V6647" "V6648"
## [6649] "V6649" "V6650" "V6651" "V6652" "V6653" "V6654" "V6655" "V6656"
## [6657] "V6657" "V6658" "V6659" "V6660" "V6661" "V6662" "V6663" "V6664"
## [6665] "V6665" "V6666" "V6667" "V6668" "V6669" "V6670" "V6671" "V6672"
## [6673] "V6673" "V6674" "V6675" "V6676" "V6677" "V6678" "V6679" "V6680"
## [6681] "V6681" "V6682" "V6683" "V6684" "V6685" "V6686" "V6687" "V6688"
## [6689] "V6689" "V6690" "V6691" "V6692" "V6693" "V6694" "V6695" "V6696"
## [6697] "V6697" "V6698" "V6699" "V6700" "V6701" "V6702" "V6703" "V6704"
## [6705] "V6705" "V6706" "V6707" "V6708" "V6709" "V6710" "V6711" "V6712"
## [6713] "V6713" "V6714" "V6715" "V6716" "V6717" "V6718" "V6719" "V6720"
## [6721] "V6721" "V6722" "V6723" "V6724" "V6725" "V6726" "V6727" "V6728"
## [6729] "V6729" "V6730" "V6731" "V6732" "V6733" "V6734" "V6735" "V6736"
## [6737] "V6737" "V6738" "V6739" "V6740" "V6741" "V6742" "V6743" "V6744"
## [6745] "V6745" "V6746" "V6747" "V6748" "V6749" "V6750" "V6751" "V6752"
## [6753] "V6753" "V6754" "V6755" "V6756" "V6757" "V6758" "V6759" "V6760"
## [6761] "V6761" "V6762" "V6763" "V6764" "V6765" "V6766" "V6767" "V6768"
## [6769] "V6769" "V6770" "V6771" "V6772" "V6773" "V6774" "V6775" "V6776"
## [6777] "V6777" "V6778" "V6779" "V6780" "V6781" "V6782" "V6783" "V6784"
## [6785] "V6785" "V6786" "V6787" "V6788" "V6789" "V6790" "V6791" "V6792"
## [6793] "V6793" "V6794" "V6795" "V6796" "V6797" "V6798" "V6799" "V6800"
## [6801] "V6801" "V6802" "V6803" "V6804" "V6805" "V6806" "V6807" "V6808"
## [6809] "V6809" "V6810" "V6811" "V6812" "V6813" "V6814" "V6815" "V6816"
## [6817] "V6817" "V6818" "V6819" "V6820" "V6821" "V6822" "V6823" "V6824"
## [6825] "V6825" "V6826" "V6827" "V6828" "V6829" "V6830" "V6831" "V6832"
## [6833] "V6833" "V6834" "V6835" "V6836" "V6837" "V6838" "V6839" "V6840"
## [6841] "V6841" "V6842" "V6843" "V6844" "V6845" "V6846" "V6847" "V6848"
## [6849] "V6849" "V6850" "V6851" "V6852" "V6853" "V6854" "V6855" "V6856"
## [6857] "V6857" "V6858" "V6859" "V6860" "V6861" "V6862" "V6863" "V6864"
## [6865] "V6865" "V6866" "V6867" "V6868" "V6869" "V6870" "V6871" "V6872"
## [6873] "V6873" "V6874" "V6875" "V6876" "V6877" "V6878" "V6879" "V6880"
## [6881] "V6881" "V6882" "V6883" "V6884" "V6885" "V6886" "V6887" "V6888"
## [6889] "V6889" "V6890" "V6891" "V6892" "V6893" "V6894" "V6895" "V6896"
## [6897] "V6897" "V6898" "V6899" "V6900" "V6901" "V6902" "V6903" "V6904"
## [6905] "V6905" "V6906" "V6907" "V6908" "V6909" "V6910" "V6911" "V6912"
## [6913] "V6913" "V6914" "V6915" "V6916" "V6917" "V6918" "V6919" "V6920"
## [6921] "V6921" "V6922" "V6923" "V6924" "V6925" "V6926" "V6927" "V6928"
## [6929] "V6929" "V6930" "V6931" "V6932" "V6933" "V6934" "V6935" "V6936"
## [6937] "V6937" "V6938" "V6939" "V6940" "V6941" "V6942" "V6943" "V6944"
## [6945] "V6945" "V6946" "V6947" "V6948" "V6949" "V6950" "V6951" "V6952"
## [6953] "V6953" "V6954" "V6955" "V6956" "V6957" "V6958" "V6959" "V6960"
## [6961] "V6961" "V6962" "V6963" "V6964" "V6965" "V6966" "V6967" "V6968"
## [6969] "V6969" "V6970" "V6971" "V6972" "V6973" "V6974" "V6975" "V6976"
## [6977] "V6977" "V6978" "V6979" "V6980" "V6981" "V6982" "V6983" "V6984"
## [6985] "V6985" "V6986" "V6987" "V6988" "V6989" "V6990" "V6991" "V6992"
## [6993] "V6993" "V6994" "V6995" "V6996" "V6997" "V6998" "V6999" "V7000"
## [7001] "V7001" "V7002" "V7003" "V7004" "V7005" "V7006" "V7007" "V7008"
## [7009] "V7009" "V7010" "V7011" "V7012" "V7013" "V7014" "V7015" "V7016"
## [7017] "V7017" "V7018" "V7019" "V7020" "V7021" "V7022" "V7023" "V7024"
## [7025] "V7025" "V7026" "V7027" "V7028" "V7029" "V7030" "V7031" "V7032"
## [7033] "V7033" "V7034" "V7035" "V7036" "V7037" "V7038" "V7039" "V7040"
## [7041] "V7041" "V7042" "V7043" "V7044" "V7045" "V7046" "V7047" "V7048"
## [7049] "V7049" "V7050" "V7051" "V7052" "V7053" "V7054" "V7055" "V7056"
## [7057] "V7057" "V7058" "V7059" "V7060" "V7061" "V7062" "V7063" "V7064"
## [7065] "V7065" "V7066" "V7067" "V7068" "V7069" "V7070" "V7071" "V7072"
## [7073] "V7073" "V7074" "V7075" "V7076" "V7077" "V7078" "V7079" "V7080"
## [7081] "V7081" "V7082" "V7083" "V7084" "V7085" "V7086" "V7087" "V7088"
## [7089] "V7089" "V7090" "V7091" "V7092" "V7093" "V7094" "V7095" "V7096"
## [7097] "V7097" "V7098" "V7099" "V7100" "V7101" "V7102" "V7103" "V7104"
## [7105] "V7105" "V7106" "V7107" "V7108" "V7109" "V7110" "V7111" "V7112"
## [7113] "V7113" "V7114" "V7115" "V7116" "V7117" "V7118" "V7119" "V7120"
## [7121] "V7121" "V7122" "V7123" "V7124" "V7125" "V7126" "V7127" "V7128"
## [7129] "V7129" "V7130" "V7131" "V7132" "V7133" "V7134" "V7135" "V7136"
## [7137] "V7137" "V7138" "V7139" "V7140" "V7141" "V7142" "V7143" "V7144"
## [7145] "V7145" "V7146" "V7147" "V7148" "V7149" "V7150" "V7151" "V7152"
## [7153] "V7153" "V7154" "V7155" "V7156" "V7157" "V7158" "V7159" "V7160"
## [7161] "V7161" "V7162" "V7163" "V7164" "V7165" "V7166" "V7167" "V7168"
## [7169] "V7169" "V7170" "V7171" "V7172" "V7173" "V7174" "V7175" "V7176"
## [7177] "V7177" "V7178" "V7179" "V7180" "V7181" "V7182" "V7183" "V7184"
## [7185] "V7185" "V7186" "V7187" "V7188" "V7189" "V7190" "V7191" "V7192"
## [7193] "V7193" "V7194" "V7195" "V7196" "V7197" "V7198" "V7199" "V7200"
## [7201] "V7201" "V7202" "V7203" "V7204" "V7205" "V7206" "V7207" "V7208"
## [7209] "V7209" "V7210" "V7211" "V7212" "V7213" "V7214" "V7215" "V7216"
## [7217] "V7217" "V7218" "V7219" "V7220" "V7221" "V7222" "V7223" "V7224"
## [7225] "V7225" "V7226" "V7227" "V7228" "V7229" "V7230" "V7231" "V7232"
## [7233] "V7233" "V7234" "V7235" "V7236" "V7237" "V7238" "V7239" "V7240"
## [7241] "V7241" "V7242" "V7243" "V7244" "V7245" "V7246" "V7247" "V7248"
## [7249] "V7249" "V7250" "V7251" "V7252" "V7253" "V7254" "V7255" "V7256"
## [7257] "V7257" "V7258" "V7259" "V7260" "V7261" "V7262" "V7263" "V7264"
## [7265] "V7265" "V7266" "V7267" "V7268" "V7269" "V7270" "V7271" "V7272"
## [7273] "V7273" "V7274" "V7275" "V7276" "V7277" "V7278" "V7279" "V7280"
## [7281] "V7281" "V7282" "V7283" "V7284" "V7285" "V7286" "V7287" "V7288"
## [7289] "V7289" "V7290" "V7291" "V7292" "V7293" "V7294" "V7295" "V7296"
## [7297] "V7297" "V7298" "V7299" "V7300" "V7301" "V7302" "V7303" "V7304"
## [7305] "V7305" "V7306" "V7307" "V7308" "V7309" "V7310" "V7311" "V7312"
## [7313] "V7313" "V7314" "V7315" "V7316" "V7317" "V7318" "V7319" "V7320"
## [7321] "V7321" "V7322" "V7323" "V7324" "V7325" "V7326" "V7327" "V7328"
## [7329] "V7329" "V7330" "V7331" "V7332" "V7333" "V7334" "V7335" "V7336"
## [7337] "V7337" "V7338" "V7339" "V7340" "V7341" "V7342" "V7343" "V7344"
## [7345] "V7345" "V7346" "V7347" "V7348" "V7349" "V7350" "V7351" "V7352"
## [7353] "V7353" "V7354" "V7355" "V7356" "V7357" "V7358" "V7359" "V7360"
## [7361] "V7361" "V7362" "V7363" "V7364" "V7365" "V7366" "V7367" "V7368"
## [7369] "V7369" "V7370" "V7371" "V7372" "V7373" "V7374" "V7375" "V7376"
## [7377] "V7377" "V7378" "V7379" "V7380" "V7381" "V7382" "V7383" "V7384"
## [7385] "V7385" "V7386" "V7387" "V7388" "V7389" "V7390" "V7391" "V7392"
## [7393] "V7393" "V7394" "V7395" "V7396" "V7397" "V7398" "V7399" "V7400"
## [7401] "V7401" "V7402" "V7403" "V7404" "V7405" "V7406" "V7407" "V7408"
## [7409] "V7409" "V7410" "V7411" "V7412" "V7413" "V7414" "V7415" "V7416"
## [7417] "V7417" "V7418" "V7419" "V7420" "V7421" "V7422" "V7423" "V7424"
## [7425] "V7425" "V7426" "V7427" "V7428" "V7429" "V7430" "V7431" "V7432"
## [7433] "V7433" "V7434" "V7435" "V7436" "V7437" "V7438" "V7439" "V7440"
## [7441] "V7441" "V7442" "V7443" "V7444" "V7445" "V7446" "V7447" "V7448"
## [7449] "V7449" "V7450" "V7451" "V7452" "V7453" "V7454" "V7455" "V7456"
## [7457] "V7457" "V7458" "V7459" "V7460" "V7461" "V7462" "V7463" "V7464"
## [7465] "V7465" "V7466" "V7467" "V7468" "V7469" "V7470" "V7471" "V7472"
## [7473] "V7473" "V7474" "V7475" "V7476" "V7477" "V7478" "V7479" "V7480"
## [7481] "V7481" "V7482" "V7483" "V7484" "V7485" "V7486" "V7487" "V7488"
## [7489] "V7489" "V7490" "V7491" "V7492" "V7493" "V7494" "V7495" "V7496"
## [7497] "V7497" "V7498" "V7499" "V7500" "V7501" "V7502" "V7503" "V7504"
## [7505] "V7505" "V7506" "V7507" "V7508" "V7509" "V7510" "V7511" "V7512"
## [7513] "V7513" "V7514" "V7515" "V7516" "V7517" "V7518" "V7519" "V7520"
## [7521] "V7521" "V7522" "V7523" "V7524" "V7525" "V7526" "V7527" "V7528"
## [7529] "V7529" "V7530" "V7531" "V7532" "V7533" "V7534" "V7535" "V7536"
## [7537] "V7537" "V7538" "V7539" "V7540" "V7541" "V7542" "V7543" "V7544"
## [7545] "V7545" "V7546" "V7547" "V7548" "V7549" "V7550" "V7551" "V7552"
## [7553] "V7553" "V7554" "V7555" "V7556" "V7557" "V7558" "V7559" "V7560"
## [7561] "V7561" "V7562" "V7563" "V7564" "V7565" "V7566" "V7567" "V7568"
## [7569] "V7569" "V7570" "V7571" "V7572" "V7573" "V7574" "V7575" "V7576"
## [7577] "V7577" "V7578" "V7579" "V7580" "V7581" "V7582" "V7583" "V7584"
## [7585] "V7585" "V7586" "V7587" "V7588" "V7589" "V7590" "V7591" "V7592"
## [7593] "V7593" "V7594" "V7595" "V7596" "V7597" "V7598" "V7599" "V7600"
## [7601] "V7601" "V7602" "V7603" "V7604" "V7605" "V7606" "V7607" "V7608"
## [7609] "V7609" "V7610" "V7611" "V7612" "V7613" "V7614" "V7615" "V7616"
## [7617] "V7617" "V7618" "V7619" "V7620" "V7621" "V7622" "V7623" "V7624"
## [7625] "V7625" "V7626" "V7627" "V7628" "V7629" "V7630" "V7631" "V7632"
## [7633] "V7633" "V7634" "V7635" "V7636" "V7637" "V7638" "V7639" "V7640"
## [7641] "V7641" "V7642" "V7643" "V7644" "V7645" "V7646" "V7647" "V7648"
## [7649] "V7649" "V7650" "V7651" "V7652" "V7653" "V7654" "V7655" "V7656"
## [7657] "V7657" "V7658" "V7659" "V7660" "V7661" "V7662" "V7663" "V7664"
## [7665] "V7665" "V7666" "V7667" "V7668" "V7669" "V7670" "V7671" "V7672"
## [7673] "V7673" "V7674" "V7675" "V7676" "V7677" "V7678" "V7679" "V7680"
## [7681] "V7681" "V7682" "V7683" "V7684" "V7685" "V7686" "V7687" "V7688"
## [7689] "V7689" "V7690" "V7691" "V7692" "V7693" "V7694" "V7695" "V7696"
## [7697] "V7697" "V7698" "V7699" "V7700" "V7701" "V7702" "V7703" "V7704"
## [7705] "V7705" "V7706" "V7707" "V7708" "V7709" "V7710" "V7711" "V7712"
## [7713] "V7713" "V7714" "V7715" "V7716" "V7717" "V7718" "V7719" "V7720"
## [7721] "V7721" "V7722" "V7723" "V7724" "V7725" "V7726" "V7727" "V7728"
## [7729] "V7729" "V7730" "V7731" "V7732" "V7733" "V7734" "V7735" "V7736"
## [7737] "V7737" "V7738" "V7739" "V7740" "V7741" "V7742" "V7743" "V7744"
## [7745] "V7745" "V7746" "V7747" "V7748" "V7749" "V7750" "V7751" "V7752"
## [7753] "V7753" "V7754" "V7755" "V7756" "V7757" "V7758" "V7759" "V7760"
## [7761] "V7761" "V7762" "V7763" "V7764" "V7765" "V7766" "V7767" "V7768"
## [7769] "V7769" "V7770" "V7771" "V7772" "V7773" "V7774" "V7775" "V7776"
## [7777] "V7777" "V7778" "V7779" "V7780" "V7781" "V7782" "V7783" "V7784"
## [7785] "V7785" "V7786" "V7787" "V7788" "V7789" "V7790" "V7791" "V7792"
## [7793] "V7793" "V7794" "V7795" "V7796" "V7797" "V7798" "V7799" "V7800"
## [7801] "V7801" "V7802" "V7803" "V7804" "V7805" "V7806" "V7807" "V7808"
## [7809] "V7809" "V7810" "V7811" "V7812" "V7813" "V7814" "V7815" "V7816"
## [7817] "V7817" "V7818" "V7819" "V7820" "V7821" "V7822" "V7823" "V7824"
## [7825] "V7825" "V7826" "V7827" "V7828" "V7829" "V7830" "V7831" "V7832"
## [7833] "V7833" "V7834" "V7835" "V7836" "V7837" "V7838" "V7839" "V7840"
## [7841] "V7841" "V7842" "V7843" "V7844" "V7845" "V7846" "V7847" "V7848"
## [7849] "V7849" "V7850" "V7851" "V7852" "V7853" "V7854" "V7855" "V7856"
## [7857] "V7857" "V7858" "V7859" "V7860" "V7861" "V7862" "V7863" "V7864"
## [7865] "V7865" "V7866" "V7867" "V7868" "V7869" "V7870" "V7871" "V7872"
## [7873] "V7873" "V7874" "V7875" "V7876" "V7877" "V7878" "V7879" "V7880"
## [7881] "V7881" "V7882" "V7883" "V7884" "V7885" "V7886" "V7887" "V7888"
## [7889] "V7889" "V7890" "V7891" "V7892" "V7893" "V7894" "V7895" "V7896"
## [7897] "V7897" "V7898" "V7899" "V7900" "V7901" "V7902" "V7903" "V7904"
## [7905] "V7905" "V7906" "V7907" "V7908" "V7909" "V7910" "V7911" "V7912"
## [7913] "V7913" "V7914" "V7915" "V7916" "V7917" "V7918" "V7919" "V7920"
## [7921] "V7921" "V7922" "V7923" "V7924" "V7925" "V7926" "V7927" "V7928"
## [7929] "V7929" "V7930" "V7931" "V7932" "V7933" "V7934" "V7935" "V7936"
## [7937] "V7937" "V7938" "V7939" "V7940" "V7941" "V7942" "V7943" "V7944"
## [7945] "V7945" "V7946" "V7947" "V7948" "V7949" "V7950" "V7951" "V7952"
## [7953] "V7953" "V7954" "V7955" "V7956" "V7957" "V7958" "V7959" "V7960"
## [7961] "V7961" "V7962" "V7963" "V7964" "V7965" "V7966" "V7967" "V7968"
## [7969] "V7969" "V7970" "V7971" "V7972" "V7973" "V7974" "V7975" "V7976"
## [7977] "V7977" "V7978" "V7979" "V7980" "V7981" "V7982" "V7983" "V7984"
## [7985] "V7985" "V7986" "V7987" "V7988" "V7989" "V7990" "V7991" "V7992"
## [7993] "V7993" "V7994" "V7995" "V7996" "V7997" "V7998" "V7999" "V8000"
## [8001] "V8001" "V8002" "V8003" "V8004" "V8005" "V8006" "V8007" "V8008"
## [8009] "V8009" "V8010" "V8011" "V8012" "V8013" "V8014" "V8015" "V8016"
## [8017] "V8017" "V8018" "V8019" "V8020" "V8021" "V8022" "V8023" "V8024"
## [8025] "V8025" "V8026" "V8027" "V8028" "V8029" "V8030" "V8031" "V8032"
## [8033] "V8033" "V8034" "V8035" "V8036" "V8037" "V8038" "V8039" "V8040"
## [8041] "V8041" "V8042" "V8043" "V8044" "V8045" "V8046" "V8047" "V8048"
## [8049] "V8049" "V8050" "V8051" "V8052" "V8053" "V8054" "V8055" "V8056"
## [8057] "V8057" "V8058" "V8059" "V8060" "V8061" "V8062" "V8063" "V8064"
## [8065] "V8065" "V8066" "V8067" "V8068" "V8069" "V8070" "V8071" "V8072"
## [8073] "V8073" "V8074" "V8075" "V8076" "V8077" "V8078" "V8079" "V8080"
## [8081] "V8081" "V8082" "V8083" "V8084" "V8085" "V8086" "V8087" "V8088"
## [8089] "V8089" "V8090" "V8091" "V8092" "V8093" "V8094" "V8095" "V8096"
## [8097] "V8097" "V8098" "V8099" "V8100" "V8101" "V8102" "V8103" "V8104"
## [8105] "V8105" "V8106" "V8107" "V8108" "V8109" "V8110" "V8111" "V8112"
## [8113] "V8113" "V8114" "V8115" "V8116" "V8117" "V8118" "V8119" "V8120"
## [8121] "V8121" "V8122" "V8123" "V8124" "V8125" "V8126" "V8127" "V8128"
## [8129] "V8129" "V8130" "V8131" "V8132" "V8133" "V8134" "V8135" "V8136"
## [8137] "V8137" "V8138" "V8139" "V8140" "V8141" "V8142" "V8143" "V8144"
## [8145] "V8145" "V8146" "V8147" "V8148" "V8149" "V8150" "V8151" "V8152"
## [8153] "V8153" "V8154" "V8155" "V8156" "V8157" "V8158" "V8159" "V8160"
## [8161] "V8161" "V8162" "V8163" "V8164" "V8165" "V8166" "V8167" "V8168"
## [8169] "V8169" "V8170" "V8171" "V8172" "V8173" "V8174" "V8175" "V8176"
## [8177] "V8177" "V8178" "V8179" "V8180" "V8181" "V8182" "V8183" "V8184"
## [8185] "V8185" "V8186" "V8187" "V8188" "V8189" "V8190" "V8191" "V8192"
## [8193] "V8193" "V8194" "V8195" "V8196" "V8197" "V8198" "V8199" "V8200"
## [8201] "V8201" "V8202" "V8203" "V8204" "V8205" "V8206" "V8207" "V8208"
## [8209] "V8209" "V8210" "V8211" "V8212" "V8213" "V8214" "V8215" "V8216"
## [8217] "V8217" "V8218" "V8219" "V8220" "V8221" "V8222" "V8223" "V8224"
## [8225] "V8225" "V8226" "V8227" "V8228" "V8229" "V8230" "V8231" "V8232"
## [8233] "V8233" "V8234" "V8235" "V8236" "V8237" "V8238" "V8239" "V8240"
## [8241] "V8241" "V8242" "V8243" "V8244" "V8245" "V8246" "V8247" "V8248"
## [8249] "V8249" "V8250" "V8251" "V8252" "V8253" "V8254" "V8255" "V8256"
## [8257] "V8257" "V8258" "V8259" "V8260" "V8261" "V8262" "V8263" "V8264"
## [8265] "V8265" "V8266" "V8267" "V8268" "V8269" "V8270" "V8271" "V8272"
## [8273] "V8273" "V8274" "V8275" "V8276" "V8277" "V8278" "V8279" "V8280"
## [8281] "V8281" "V8282" "V8283" "V8284" "V8285" "V8286" "V8287" "V8288"
## [8289] "V8289" "V8290" "V8291" "V8292" "V8293" "V8294" "V8295" "V8296"
## [8297] "V8297" "V8298" "V8299" "V8300" "V8301" "V8302" "V8303" "V8304"
## [8305] "V8305" "V8306" "V8307" "V8308" "V8309" "V8310" "V8311" "V8312"
## [8313] "V8313" "V8314" "V8315" "V8316" "V8317" "V8318" "V8319" "V8320"
## [8321] "V8321" "V8322" "V8323" "V8324" "V8325" "V8326" "V8327" "V8328"
## [8329] "V8329" "V8330" "V8331" "V8332" "V8333" "V8334" "V8335" "V8336"
## [8337] "V8337" "V8338" "V8339" "V8340" "V8341" "V8342" "V8343" "V8344"
## [8345] "V8345" "V8346" "V8347" "V8348" "V8349" "V8350" "V8351" "V8352"
## [8353] "V8353" "V8354" "V8355" "V8356" "V8357" "V8358" "V8359" "V8360"
## [8361] "V8361" "V8362" "V8363" "V8364" "V8365" "V8366" "V8367" "V8368"
## [8369] "V8369" "V8370" "V8371" "V8372" "V8373" "V8374" "V8375" "V8376"
## [8377] "V8377" "V8378" "V8379" "V8380" "V8381" "V8382" "V8383" "V8384"
## [8385] "V8385" "V8386" "V8387" "V8388" "V8389" "V8390" "V8391" "V8392"
## [8393] "V8393" "V8394" "V8395" "V8396" "V8397" "V8398" "V8399" "V8400"
## [8401] "V8401" "V8402" "V8403" "V8404" "V8405" "V8406" "V8407" "V8408"
## [8409] "V8409" "V8410" "V8411" "V8412" "V8413" "V8414" "V8415" "V8416"
## [8417] "V8417" "V8418" "V8419" "V8420" "V8421" "V8422" "V8423" "V8424"
## [8425] "V8425" "V8426" "V8427" "V8428" "V8429" "V8430" "V8431" "V8432"
## [8433] "V8433" "V8434" "V8435" "V8436" "V8437" "V8438" "V8439" "V8440"
## [8441] "V8441" "V8442" "V8443" "V8444" "V8445" "V8446" "V8447" "V8448"
## [8449] "V8449" "V8450" "V8451" "V8452" "V8453" "V8454" "V8455" "V8456"
## [8457] "V8457" "V8458" "V8459" "V8460" "V8461" "V8462" "V8463" "V8464"
## [8465] "V8465" "V8466" "V8467" "V8468" "V8469" "V8470" "V8471" "V8472"
## [8473] "V8473" "V8474" "V8475" "V8476" "V8477" "V8478" "V8479" "V8480"
## [8481] "V8481" "V8482" "V8483" "V8484" "V8485" "V8486" "V8487" "V8488"
## [8489] "V8489" "V8490" "V8491" "V8492" "V8493" "V8494" "V8495" "V8496"
## [8497] "V8497" "V8498" "V8499" "V8500" "V8501" "V8502" "V8503" "V8504"
## [8505] "V8505" "V8506" "V8507" "V8508" "V8509" "V8510" "V8511" "V8512"
## [8513] "V8513" "V8514" "V8515" "V8516" "V8517" "V8518" "V8519" "V8520"
## [8521] "V8521" "V8522" "V8523" "V8524" "V8525" "V8526" "V8527" "V8528"
## [8529] "V8529" "V8530" "V8531" "V8532" "V8533" "V8534" "V8535" "V8536"
## [8537] "V8537" "V8538" "V8539" "V8540" "V8541" "V8542" "V8543" "V8544"
## [8545] "V8545" "V8546" "V8547" "V8548" "V8549" "V8550" "V8551" "V8552"
## [8553] "V8553" "V8554" "V8555" "V8556" "V8557" "V8558" "V8559" "V8560"
## [8561] "V8561" "V8562" "V8563" "V8564" "V8565" "V8566" "V8567" "V8568"
## [8569] "V8569" "V8570" "V8571" "V8572" "V8573" "V8574" "V8575" "V8576"
## [8577] "V8577" "V8578" "V8579" "V8580" "V8581" "V8582" "V8583" "V8584"
## [8585] "V8585" "V8586" "V8587" "V8588" "V8589" "V8590" "V8591" "V8592"
## [8593] "V8593" "V8594" "V8595" "V8596" "V8597" "V8598" "V8599" "V8600"
## [8601] "V8601" "V8602" "V8603" "V8604" "V8605" "V8606" "V8607" "V8608"
## [8609] "V8609" "V8610" "V8611" "V8612" "V8613" "V8614" "V8615" "V8616"
## [8617] "V8617" "V8618" "V8619" "V8620" "V8621" "V8622" "V8623" "V8624"
## [8625] "V8625" "V8626" "V8627" "V8628" "V8629" "V8630" "V8631" "V8632"
## [8633] "V8633" "V8634" "V8635" "V8636" "V8637" "V8638" "V8639" "V8640"
## [8641] "V8641" "V8642" "V8643" "V8644" "V8645" "V8646" "V8647" "V8648"
## [8649] "V8649" "V8650" "V8651" "V8652" "V8653" "V8654" "V8655" "V8656"
## [8657] "V8657" "V8658" "V8659" "V8660" "V8661" "V8662" "V8663" "V8664"
## [8665] "V8665" "V8666" "V8667" "V8668" "V8669" "V8670" "V8671" "V8672"
## [8673] "V8673" "V8674" "V8675" "V8676" "V8677" "V8678" "V8679" "V8680"
## [8681] "V8681" "V8682" "V8683" "V8684" "V8685" "V8686" "V8687" "V8688"
## [8689] "V8689" "V8690" "V8691" "V8692" "V8693" "V8694" "V8695" "V8696"
## [8697] "V8697" "V8698" "V8699" "V8700" "V8701" "V8702" "V8703" "V8704"
## [8705] "V8705" "V8706" "V8707" "V8708" "V8709" "V8710" "V8711" "V8712"
## [8713] "V8713" "V8714" "V8715" "V8716" "V8717" "V8718" "V8719" "V8720"
## [8721] "V8721" "V8722" "V8723" "V8724" "V8725" "V8726" "V8727" "V8728"
## [8729] "V8729" "V8730" "V8731" "V8732" "V8733" "V8734" "V8735" "V8736"
## [8737] "V8737" "V8738" "V8739" "V8740" "V8741" "V8742" "V8743" "V8744"
## [8745] "V8745" "V8746" "V8747" "V8748" "V8749" "V8750" "V8751" "V8752"
## [8753] "V8753" "V8754" "V8755" "V8756" "V8757" "V8758" "V8759" "V8760"
## [8761] "V8761" "V8762" "V8763" "V8764" "V8765" "V8766" "V8767" "V8768"
## [8769] "V8769" "V8770" "V8771" "V8772" "V8773" "V8774" "V8775" "V8776"
## [8777] "V8777" "V8778" "V8779" "V8780" "V8781" "V8782" "V8783" "V8784"
## [8785] "V8785" "V8786" "V8787" "V8788" "V8789" "V8790" "V8791" "V8792"
## [8793] "V8793" "V8794" "V8795" "V8796" "V8797" "V8798" "V8799" "V8800"
## [8801] "V8801" "V8802" "V8803" "V8804" "V8805" "V8806" "V8807" "V8808"
## [8809] "V8809" "V8810" "V8811" "V8812" "V8813" "V8814" "V8815" "V8816"
## [8817] "V8817" "V8818" "V8819" "V8820" "V8821" "V8822" "V8823" "V8824"
## [8825] "V8825" "V8826" "V8827" "V8828" "V8829" "V8830" "V8831" "V8832"
## [8833] "V8833" "V8834" "V8835" "V8836" "V8837" "V8838" "V8839" "V8840"
## [8841] "V8841" "V8842" "V8843" "V8844" "V8845" "V8846" "V8847" "V8848"
## [8849] "V8849" "V8850" "V8851" "V8852" "V8853" "V8854" "V8855" "V8856"
## [8857] "V8857" "V8858" "V8859" "V8860" "V8861" "V8862" "V8863" "V8864"
## [8865] "V8865" "V8866" "V8867" "V8868" "V8869" "V8870" "V8871" "V8872"
## [8873] "V8873" "V8874" "V8875" "V8876" "V8877" "V8878" "V8879" "V8880"
## [8881] "V8881" "V8882" "V8883" "V8884" "V8885" "V8886" "V8887" "V8888"
## [8889] "V8889" "V8890" "V8891" "V8892" "V8893" "V8894" "V8895" "V8896"
## [8897] "V8897" "V8898" "V8899" "V8900" "V8901" "V8902" "V8903" "V8904"
## [8905] "V8905" "V8906" "V8907" "V8908" "V8909" "V8910" "V8911" "V8912"
## [8913] "V8913" "V8914" "V8915" "V8916" "V8917" "V8918" "V8919" "V8920"
## [8921] "V8921" "V8922" "V8923" "V8924" "V8925" "V8926" "V8927" "V8928"
## [8929] "V8929" "V8930" "V8931" "V8932" "V8933" "V8934" "V8935" "V8936"
## [8937] "V8937" "V8938" "V8939" "V8940" "V8941" "V8942" "V8943" "V8944"
## [8945] "V8945" "V8946" "V8947" "V8948" "V8949" "V8950" "V8951" "V8952"
## [8953] "V8953" "V8954" "V8955" "V8956" "V8957" "V8958" "V8959" "V8960"
## [8961] "V8961" "V8962" "V8963" "V8964" "V8965" "V8966" "V8967" "V8968"
## [8969] "V8969" "V8970" "V8971" "V8972" "V8973" "V8974" "V8975" "V8976"
## [8977] "V8977" "V8978" "V8979" "V8980" "V8981" "V8982" "V8983" "V8984"
## [8985] "V8985" "V8986" "V8987" "V8988" "V8989" "V8990" "V8991" "V8992"
## [8993] "V8993" "V8994" "V8995" "V8996" "V8997" "V8998" "V8999" "V9000"
## [9001] "V9001" "V9002" "V9003" "V9004" "V9005" "V9006" "V9007" "V9008"
## [9009] "V9009" "V9010" "V9011" "V9012" "V9013" "V9014" "V9015" "V9016"
## [9017] "V9017" "V9018" "V9019" "V9020" "V9021" "V9022" "V9023" "V9024"
## [9025] "V9025" "V9026" "V9027" "V9028" "V9029" "V9030" "V9031" "V9032"
## [9033] "V9033" "V9034" "V9035" "V9036" "V9037" "V9038" "V9039" "V9040"
## [9041] "V9041" "V9042" "V9043" "V9044" "V9045" "V9046" "V9047" "V9048"
## [9049] "V9049" "V9050" "V9051" "V9052" "V9053" "V9054" "V9055" "V9056"
## [9057] "V9057" "V9058" "V9059" "V9060" "V9061" "V9062" "V9063" "V9064"
## [9065] "V9065" "V9066" "V9067" "V9068" "V9069" "V9070" "V9071" "V9072"
## [9073] "V9073" "V9074" "V9075" "V9076" "V9077" "V9078" "V9079" "V9080"
## [9081] "V9081" "V9082" "V9083" "V9084" "V9085" "V9086" "V9087" "V9088"
## [9089] "V9089" "V9090" "V9091" "V9092" "V9093" "V9094" "V9095" "V9096"
## [9097] "V9097" "V9098" "V9099" "V9100" "V9101" "V9102" "V9103" "V9104"
## [9105] "V9105" "V9106" "V9107" "V9108" "V9109" "V9110" "V9111" "V9112"
## [9113] "V9113" "V9114" "V9115" "V9116" "V9117" "V9118" "V9119" "V9120"
## [9121] "V9121" "V9122" "V9123" "V9124" "V9125" "V9126" "V9127" "V9128"
## [9129] "V9129" "V9130" "V9131" "V9132" "V9133" "V9134" "V9135" "V9136"
## [9137] "V9137" "V9138" "V9139" "V9140" "V9141" "V9142" "V9143" "V9144"
## [9145] "V9145" "V9146" "V9147" "V9148" "V9149" "V9150" "V9151" "V9152"
## [9153] "V9153" "V9154" "V9155" "V9156" "V9157" "V9158" "V9159" "V9160"
## [9161] "V9161" "V9162" "V9163" "V9164" "V9165" "V9166" "V9167" "V9168"
## [9169] "V9169" "V9170" "V9171" "V9172" "V9173" "V9174" "V9175" "V9176"
## [9177] "V9177" "V9178" "V9179" "V9180" "V9181" "V9182" "V9183" "V9184"
## [9185] "V9185" "V9186" "V9187" "V9188" "V9189" "V9190" "V9191" "V9192"
## [9193] "V9193" "V9194" "V9195" "V9196" "V9197" "V9198" "V9199" "V9200"
## [9201] "V9201" "V9202" "V9203" "V9204" "V9205" "V9206" "V9207" "V9208"
## [9209] "V9209" "V9210" "V9211" "V9212" "V9213" "V9214" "V9215" "V9216"
## [9217] "V9217" "V9218" "V9219" "V9220" "V9221" "V9222" "V9223" "V9224"
## [9225] "V9225" "V9226" "V9227" "V9228" "V9229" "V9230" "V9231" "V9232"
## [9233] "V9233" "V9234" "V9235" "V9236" "V9237" "V9238" "V9239" "V9240"
## [9241] "V9241" "V9242" "V9243" "V9244" "V9245" "V9246" "V9247" "V9248"
## [9249] "V9249" "V9250" "V9251" "V9252" "V9253" "V9254" "V9255" "V9256"
## [9257] "V9257" "V9258" "V9259" "V9260" "V9261" "V9262" "V9263" "V9264"
## [9265] "V9265" "V9266" "V9267" "V9268" "V9269" "V9270" "V9271" "V9272"
## [9273] "V9273" "V9274" "V9275" "V9276" "V9277" "V9278" "V9279" "V9280"
## [9281] "V9281" "V9282" "V9283" "V9284" "V9285" "V9286" "V9287" "V9288"
## [9289] "V9289" "V9290" "V9291" "V9292" "V9293" "V9294" "V9295" "V9296"
## [9297] "V9297" "V9298" "V9299" "V9300" "V9301" "V9302" "V9303" "V9304"
## [9305] "V9305" "V9306" "V9307" "V9308" "V9309" "V9310" "V9311" "V9312"
## [9313] "V9313" "V9314" "V9315" "V9316" "V9317" "V9318" "V9319" "V9320"
## [9321] "V9321" "V9322" "V9323" "V9324" "V9325" "V9326" "V9327" "V9328"
## [9329] "V9329" "V9330" "V9331" "V9332" "V9333" "V9334" "V9335" "V9336"
## [9337] "V9337" "V9338" "V9339" "V9340" "V9341" "V9342" "V9343" "V9344"
## [9345] "V9345" "V9346" "V9347" "V9348" "V9349" "V9350" "V9351" "V9352"
## [9353] "V9353" "V9354" "V9355" "V9356" "V9357" "V9358" "V9359" "V9360"
## [9361] "V9361" "V9362" "V9363" "V9364" "V9365" "V9366" "V9367" "V9368"
## [9369] "V9369" "V9370" "V9371" "V9372" "V9373" "V9374" "V9375" "V9376"
## [9377] "V9377" "V9378" "V9379" "V9380" "V9381" "V9382" "V9383" "V9384"
## [9385] "V9385" "V9386" "V9387" "V9388" "V9389" "V9390" "V9391" "V9392"
## [9393] "V9393" "V9394" "V9395" "V9396" "V9397" "V9398" "V9399" "V9400"
## [9401] "V9401" "V9402" "V9403" "V9404" "V9405" "V9406" "V9407" "V9408"
## [9409] "V9409" "V9410" "V9411" "V9412" "V9413" "V9414" "V9415" "V9416"
## [9417] "V9417" "V9418" "V9419" "V9420" "V9421" "V9422" "V9423" "V9424"
## [9425] "V9425" "V9426" "V9427" "V9428" "V9429" "V9430" "V9431" "V9432"
## [9433] "V9433" "V9434" "V9435" "V9436" "V9437" "V9438" "V9439" "V9440"
## [9441] "V9441" "V9442" "V9443" "V9444" "V9445" "V9446" "V9447" "V9448"
## [9449] "V9449" "V9450" "V9451" "V9452" "V9453" "V9454" "V9455" "V9456"
## [9457] "V9457" "V9458" "V9459" "V9460" "V9461" "V9462" "V9463" "V9464"
## [9465] "V9465" "V9466" "V9467" "V9468" "V9469" "V9470" "V9471" "V9472"
## [9473] "V9473" "V9474" "V9475" "V9476" "V9477" "V9478" "V9479" "V9480"
## [9481] "V9481" "V9482" "V9483" "V9484" "V9485" "V9486" "V9487" "V9488"
## [9489] "V9489" "V9490" "V9491" "V9492" "V9493" "V9494" "V9495" "V9496"
## [9497] "V9497" "V9498" "V9499" "V9500" "V9501" "V9502" "V9503" "V9504"
## [9505] "V9505" "V9506" "V9507" "V9508" "V9509" "V9510" "V9511" "V9512"
## [9513] "V9513" "V9514" "V9515" "V9516" "V9517" "V9518" "V9519" "V9520"
## [9521] "V9521" "V9522" "V9523" "V9524" "V9525" "V9526" "V9527" "V9528"
## [9529] "V9529" "V9530" "V9531" "V9532" "V9533" "V9534" "V9535" "V9536"
## [9537] "V9537" "V9538" "V9539" "V9540" "V9541" "V9542" "V9543" "V9544"
## [9545] "V9545" "V9546" "V9547" "V9548" "V9549" "V9550" "V9551" "V9552"
## [9553] "V9553" "V9554" "V9555" "V9556" "V9557" "V9558" "V9559" "V9560"
## [9561] "V9561" "V9562" "V9563" "V9564" "V9565" "V9566" "V9567" "V9568"
## [9569] "V9569" "V9570" "V9571" "V9572" "V9573" "V9574" "V9575" "V9576"
## [9577] "V9577" "V9578" "V9579" "V9580" "V9581" "V9582" "V9583" "V9584"
## [9585] "V9585" "V9586" "V9587" "V9588" "V9589" "V9590" "V9591" "V9592"
## [9593] "V9593" "V9594" "V9595" "V9596" "V9597" "V9598" "V9599" "V9600"
## [9601] "V9601" "V9602" "V9603" "V9604" "V9605" "V9606" "V9607" "V9608"
## [9609] "V9609" "V9610" "V9611" "V9612" "V9613" "V9614" "V9615" "V9616"
## [9617] "V9617" "V9618" "V9619" "V9620" "V9621" "V9622" "V9623" "V9624"
## [9625] "V9625" "V9626" "V9627" "V9628" "V9629" "V9630" "V9631" "V9632"
## [9633] "V9633" "V9634" "V9635" "V9636" "V9637" "V9638" "V9639" "V9640"
## [9641] "V9641" "V9642" "V9643" "V9644" "V9645" "V9646" "V9647" "V9648"
## [9649] "V9649" "V9650" "V9651" "V9652" "V9653" "V9654" "V9655" "V9656"
## [9657] "V9657" "V9658" "V9659" "V9660" "V9661" "V9662" "V9663" "V9664"
## [9665] "V9665" "V9666" "V9667" "V9668" "V9669" "V9670" "V9671" "V9672"
## [9673] "V9673" "V9674" "V9675" "V9676" "V9677" "V9678" "V9679" "V9680"
## [9681] "V9681" "V9682" "V9683" "V9684" "V9685" "V9686" "V9687" "V9688"
## [9689] "V9689" "V9690" "V9691" "V9692" "V9693" "V9694" "V9695" "V9696"
## [9697] "V9697" "V9698" "V9699" "V9700" "V9701" "V9702" "V9703" "V9704"
## [9705] "V9705" "V9706" "V9707" "V9708" "V9709" "V9710" "V9711" "V9712"
## [9713] "V9713" "V9714" "V9715" "V9716" "V9717" "V9718" "V9719" "V9720"
## [9721] "V9721" "V9722" "V9723" "V9724" "V9725" "V9726" "V9727" "V9728"
## [9729] "V9729" "V9730" "V9731" "V9732" "V9733" "V9734" "V9735" "V9736"
## [9737] "V9737" "V9738" "V9739" "V9740" "V9741" "V9742" "V9743" "V9744"
## [9745] "V9745" "V9746" "V9747" "V9748" "V9749" "V9750" "V9751" "V9752"
## [9753] "V9753" "V9754" "V9755" "V9756" "V9757" "V9758" "V9759" "V9760"
## [9761] "V9761" "V9762" "V9763" "V9764" "V9765" "V9766" "V9767" "V9768"
## [9769] "V9769" "V9770" "V9771" "V9772" "V9773" "V9774" "V9775" "V9776"
## [9777] "V9777" "V9778" "V9779" "V9780" "V9781" "V9782" "V9783" "V9784"
## [9785] "V9785" "V9786" "V9787" "V9788" "V9789" "V9790" "V9791" "V9792"
## [9793] "V9793" "V9794" "V9795" "V9796" "V9797" "V9798" "V9799" "V9800"
## [9801] "V9801" "V9802" "V9803" "V9804" "V9805" "V9806" "V9807" "V9808"
## [9809] "V9809" "V9810" "V9811" "V9812" "V9813" "V9814" "V9815" "V9816"
## [9817] "V9817" "V9818" "V9819" "V9820" "V9821" "V9822" "V9823" "V9824"
## [9825] "V9825" "V9826" "V9827" "V9828" "V9829" "V9830" "V9831" "V9832"
## [9833] "V9833" "V9834" "V9835" "V9836" "V9837" "V9838" "V9839" "V9840"
## [9841] "V9841" "V9842" "V9843" "V9844" "V9845" "V9846" "V9847" "V9848"
## [9849] "V9849" "V9850" "V9851" "V9852" "V9853" "V9854" "V9855" "V9856"
## [9857] "V9857" "V9858" "V9859" "V9860" "V9861" "V9862" "V9863" "V9864"
## [9865] "V9865" "V9866" "V9867" "V9868" "V9869" "V9870" "V9871" "V9872"
## [9873] "V9873" "V9874" "V9875" "V9876" "V9877" "V9878" "V9879" "V9880"
## [9881] "V9881" "V9882" "V9883" "V9884" "V9885" "V9886" "V9887" "V9888"
## [9889] "V9889" "V9890" "V9891" "V9892" "V9893" "V9894" "V9895" "V9896"
## [9897] "V9897" "V9898" "V9899" "V9900" "V9901" "V9902" "V9903" "V9904"
## [9905] "V9905" "V9906" "V9907" "V9908" "V9909" "V9910" "V9911" "V9912"
## [9913] "V9913" "V9914" "V9915" "V9916" "V9917" "V9918" "V9919" "V9920"
## [9921] "V9921" "V9922" "V9923" "V9924" "V9925" "V9926" "V9927" "V9928"
## [9929] "V9929" "V9930" "V9931" "V9932" "V9933" "V9934" "V9935" "V9936"
## [9937] "V9937" "V9938" "V9939" "V9940" "V9941" "V9942" "V9943" "V9944"
## [9945] "V9945" "V9946" "V9947" "V9948" "V9949" "V9950" "V9951" "V9952"
## [9953] "V9953" "V9954" "V9955" "V9956" "V9957" "V9958" "V9959" "V9960"
## [9961] "V9961" "V9962" "V9963" "V9964" "V9965" "V9966" "V9967" "V9968"
## [9969] "V9969" "V9970" "V9971" "V9972" "V9973" "V9974" "V9975" "V9976"
## [9977] "V9977" "V9978" "V9979" "V9980" "V9981" "V9982" "V9983" "V9984"
## [9985] "V9985" "V9986" "V9987" "V9988" "V9989" "V9990" "V9991" "V9992"
## [9993] "V9993" "V9994" "V9995" "V9996" "V9997" "V9998" "V9999" "V10000"
## [10001] "V10001" "V10002" "V10003" "V10004" "V10005" "V10006" "V10007" "V10008"
## [10009] "V10009" "V10010" "V10011" "V10012" "V10013" "V10014" "V10015" "V10016"
## [10017] "V10017" "V10018" "V10019" "V10020" "V10021" "V10022" "V10023" "V10024"
## [10025] "V10025" "V10026" "V10027" "V10028" "V10029" "V10030" "V10031" "V10032"
## [10033] "V10033" "V10034" "V10035" "V10036" "V10037" "V10038" "V10039" "V10040"
## [10041] "V10041" "V10042" "V10043" "V10044" "V10045" "V10046" "V10047" "V10048"
## [10049] "V10049" "V10050" "V10051" "V10052" "V10053" "V10054" "V10055" "V10056"
## [10057] "V10057" "V10058" "V10059" "V10060" "V10061" "V10062" "V10063" "V10064"
## [10065] "V10065" "V10066" "V10067" "V10068" "V10069" "V10070" "V10071" "V10072"
## [10073] "V10073" "V10074" "V10075" "V10076" "V10077" "V10078" "V10079" "V10080"
## [10081] "V10081" "V10082" "V10083" "V10084" "V10085" "V10086" "V10087" "V10088"
## [10089] "V10089" "V10090" "V10091" "V10092" "V10093" "V10094" "V10095" "V10096"
## [10097] "V10097" "V10098" "V10099" "V10100" "V10101" "V10102" "V10103" "V10104"
## [10105] "V10105" "V10106" "V10107" "V10108" "V10109" "V10110" "V10111" "V10112"
## [10113] "V10113" "V10114" "V10115" "V10116" "V10117" "V10118" "V10119" "V10120"
## [10121] "V10121" "V10122" "V10123" "V10124" "V10125" "V10126" "V10127" "V10128"
## [10129] "V10129" "V10130" "V10131" "V10132" "V10133" "V10134" "V10135" "V10136"
## [10137] "V10137" "V10138" "V10139" "V10140" "V10141" "V10142" "V10143" "V10144"
## [10145] "V10145" "V10146" "V10147" "V10148" "V10149" "V10150" "V10151" "V10152"
## [10153] "V10153" "V10154" "V10155" "V10156" "V10157" "V10158" "V10159" "V10160"
## [10161] "V10161" "V10162" "V10163" "V10164" "V10165" "V10166" "V10167" "V10168"
## [10169] "V10169" "V10170" "V10171" "V10172" "V10173" "V10174" "V10175" "V10176"
## [10177] "V10177" "V10178" "V10179" "V10180" "V10181" "V10182" "V10183" "V10184"
## [10185] "V10185" "V10186" "V10187" "V10188" "V10189" "V10190" "V10191" "V10192"
## [10193] "V10193" "V10194" "V10195" "V10196" "V10197" "V10198" "V10199" "V10200"
## [10201] "V10201" "V10202" "V10203" "V10204" "V10205" "V10206" "V10207" "V10208"
## [10209] "V10209" "V10210" "V10211" "V10212" "V10213" "V10214" "V10215" "V10216"
## [10217] "V10217" "V10218" "V10219" "V10220" "V10221" "V10222" "V10223" "V10224"
## [10225] "V10225" "V10226" "V10227" "V10228" "V10229" "V10230" "V10231" "V10232"
## [10233] "V10233" "V10234" "V10235" "V10236" "V10237" "V10238" "V10239" "V10240"
## [10241] "V10241" "V10242" "V10243" "V10244" "V10245" "V10246" "V10247" "V10248"
## [10249] "V10249" "V10250" "V10251" "V10252" "V10253" "V10254" "V10255" "V10256"
## [10257] "V10257" "V10258" "V10259" "V10260" "V10261" "V10262" "V10263" "V10264"
## [10265] "V10265" "V10266" "V10267" "V10268" "V10269" "V10270" "V10271" "V10272"
## [10273] "V10273" "V10274" "V10275" "V10276" "V10277" "V10278" "V10279" "V10280"
## [10281] "V10281" "V10282" "V10283" "V10284" "V10285" "V10286" "V10287" "V10288"
## [10289] "V10289" "V10290" "V10291" "V10292" "V10293" "V10294" "V10295" "V10296"
## [10297] "V10297" "V10298" "V10299" "V10300" "V10301" "V10302" "V10303" "V10304"
## [10305] "V10305" "V10306" "V10307" "V10308" "V10309" "V10310" "V10311" "V10312"
## [10313] "V10313" "V10314" "V10315" "V10316" "V10317" "V10318" "V10319" "V10320"
## [10321] "V10321" "V10322" "V10323" "V10324" "V10325" "V10326" "V10327" "V10328"
## [10329] "V10329" "V10330" "V10331" "V10332" "V10333" "V10334" "V10335" "V10336"
## [10337] "V10337" "V10338" "V10339" "V10340" "V10341" "V10342" "V10343" "V10344"
## [10345] "V10345" "V10346" "V10347" "V10348" "V10349" "V10350" "V10351" "V10352"
## [10353] "V10353" "V10354" "V10355" "V10356" "V10357" "V10358" "V10359" "V10360"
## [10361] "V10361" "V10362" "V10363" "V10364" "V10365" "V10366" "V10367" "V10368"
## [10369] "V10369" "V10370" "V10371" "V10372" "V10373" "V10374" "V10375" "V10376"
## [10377] "V10377" "V10378" "V10379" "V10380" "V10381" "V10382" "V10383" "V10384"
## [10385] "V10385" "V10386" "V10387" "V10388" "V10389" "V10390" "V10391" "V10392"
## [10393] "V10393" "V10394" "V10395" "V10396" "V10397" "V10398" "V10399" "V10400"
## [10401] "V10401" "V10402" "V10403" "V10404" "V10405" "V10406" "V10407" "V10408"
## [10409] "V10409" "V10410" "V10411" "V10412" "V10413" "V10414" "V10415" "V10416"
## [10417] "V10417" "V10418" "V10419" "V10420" "V10421" "V10422" "V10423" "V10424"
## [10425] "V10425" "V10426" "V10427" "V10428" "V10429" "V10430" "V10431" "V10432"
## [10433] "V10433" "V10434" "V10435" "V10436" "V10437" "V10438" "V10439" "V10440"
## [10441] "V10441" "V10442" "V10443" "V10444" "V10445" "V10446" "V10447" "V10448"
## [10449] "V10449" "V10450" "V10451" "V10452" "V10453" "V10454" "V10455" "V10456"
## [10457] "V10457" "V10458" "V10459" "V10460" "V10461" "V10462" "V10463" "V10464"
## [10465] "V10465" "V10466" "V10467" "V10468" "V10469" "V10470" "V10471" "V10472"
## [10473] "V10473" "V10474" "V10475" "V10476" "V10477" "V10478" "V10479" "V10480"
## [10481] "V10481" "V10482" "V10483" "V10484" "V10485" "V10486" "V10487" "V10488"
## [10489] "V10489" "V10490" "V10491" "V10492" "V10493" "V10494" "V10495" "V10496"
## [10497] "V10497" "V10498" "V10499" "V10500" "V10501" "V10502" "V10503" "V10504"
## [10505] "V10505" "V10506" "V10507" "V10508" "V10509" "V10510" "V10511" "V10512"
## [10513] "V10513" "V10514" "V10515" "V10516" "V10517" "V10518" "V10519" "V10520"
## [10521] "V10521" "V10522" "V10523" "V10524" "V10525" "V10526" "V10527" "V10528"
## [10529] "V10529" "V10530" "V10531" "V10532" "V10533" "V10534" "V10535" "V10536"
## [10537] "V10537" "V10538" "V10539" "V10540" "V10541" "V10542" "V10543" "V10544"
## [10545] "V10545" "V10546" "V10547" "V10548" "V10549" "V10550" "V10551" "V10552"
## [10553] "V10553" "V10554" "V10555" "V10556" "V10557" "V10558" "V10559" "V10560"
## [10561] "V10561" "V10562" "V10563" "V10564" "V10565" "V10566" "V10567" "V10568"
## [10569] "V10569" "V10570" "V10571" "V10572" "V10573" "V10574" "V10575" "V10576"
## [10577] "V10577" "V10578" "V10579" "V10580" "V10581" "V10582" "V10583" "V10584"
## [10585] "V10585" "V10586" "V10587" "V10588" "V10589" "V10590" "V10591" "V10592"
## [10593] "V10593" "V10594" "V10595" "V10596" "V10597" "V10598" "V10599" "V10600"
## [10601] "V10601" "V10602" "V10603" "V10604" "V10605" "V10606" "V10607" "V10608"
## [10609] "V10609" "V10610" "V10611" "V10612" "V10613" "V10614" "V10615" "V10616"
## [10617] "V10617" "V10618" "V10619" "V10620" "V10621" "V10622" "V10623" "V10624"
## [10625] "V10625" "V10626" "V10627" "V10628" "V10629" "V10630" "V10631" "V10632"
## [10633] "V10633" "V10634" "V10635" "V10636" "V10637" "V10638" "V10639" "V10640"
## [10641] "V10641" "V10642" "V10643" "V10644" "V10645" "V10646" "V10647" "V10648"
## [10649] "V10649" "V10650" "V10651" "V10652" "V10653" "V10654" "V10655" "V10656"
## [10657] "V10657" "V10658" "V10659" "V10660" "V10661" "V10662" "V10663" "V10664"
## [10665] "V10665" "V10666" "V10667" "V10668" "V10669" "V10670" "V10671" "V10672"
## [10673] "V10673" "V10674" "V10675" "V10676" "V10677" "V10678" "V10679" "V10680"
## [10681] "V10681" "V10682" "V10683" "V10684" "V10685" "V10686" "V10687" "V10688"
## [10689] "V10689" "V10690" "V10691" "V10692" "V10693" "V10694" "V10695" "V10696"
## [10697] "V10697" "V10698" "V10699" "V10700" "V10701" "V10702" "V10703" "V10704"
## [10705] "V10705" "V10706" "V10707" "V10708" "V10709" "V10710" "V10711" "V10712"
## [10713] "V10713" "V10714" "V10715" "V10716" "V10717" "V10718" "V10719" "V10720"
## [10721] "V10721" "V10722" "V10723" "V10724" "V10725" "V10726" "V10727" "V10728"
## [10729] "V10729" "V10730" "V10731" "V10732" "V10733" "V10734" "V10735" "V10736"
## [10737] "V10737" "V10738" "V10739" "V10740" "V10741" "V10742" "V10743" "V10744"
## [10745] "V10745" "V10746" "V10747" "V10748" "V10749" "V10750" "V10751" "V10752"
## [10753] "V10753" "V10754" "V10755" "V10756" "V10757" "V10758" "V10759" "V10760"
## [10761] "V10761" "V10762" "V10763" "V10764" "V10765" "V10766" "V10767" "V10768"
## [10769] "V10769" "V10770" "V10771" "V10772" "V10773" "V10774" "V10775" "V10776"
## [10777] "V10777" "V10778" "V10779" "V10780" "V10781" "V10782" "V10783" "V10784"
## [10785] "V10785" "V10786" "V10787" "V10788" "V10789" "V10790" "V10791" "V10792"
## [10793] "V10793" "V10794" "V10795" "V10796" "V10797" "V10798" "V10799" "V10800"
## [10801] "V10801" "V10802" "V10803" "V10804" "V10805" "V10806" "V10807" "V10808"
## [10809] "V10809" "V10810" "V10811" "V10812" "V10813" "V10814" "V10815" "V10816"
## [10817] "V10817" "V10818" "V10819" "V10820" "V10821" "V10822" "V10823" "V10824"
## [10825] "V10825" "V10826" "V10827" "V10828" "V10829" "V10830" "V10831" "V10832"
## [10833] "V10833" "V10834" "V10835" "V10836" "V10837" "V10838" "V10839" "V10840"
## [10841] "V10841" "V10842" "V10843" "V10844" "V10845" "V10846" "V10847" "V10848"
## [10849] "V10849" "V10850" "V10851" "V10852" "V10853" "V10854" "V10855" "V10856"
## [10857] "V10857" "V10858" "V10859" "V10860" "V10861" "V10862" "V10863" "V10864"
## [10865] "V10865" "V10866" "V10867" "V10868" "V10869" "V10870" "V10871" "V10872"
## [10873] "V10873" "V10874" "V10875" "V10876" "V10877" "V10878" "V10879" "V10880"
## [10881] "V10881" "V10882" "V10883" "V10884" "V10885" "V10886" "V10887" "V10888"
## [10889] "V10889" "V10890" "V10891" "V10892" "V10893" "V10894" "V10895" "V10896"
## [10897] "V10897" "V10898" "V10899" "V10900" "V10901" "V10902" "V10903" "V10904"
## [10905] "V10905" "V10906" "V10907" "V10908" "V10909" "V10910" "V10911" "V10912"
## [10913] "V10913" "V10914" "V10915" "V10916" "V10917" "V10918" "V10919" "V10920"
## [10921] "V10921" "V10922" "V10923" "V10924" "V10925" "V10926" "V10927" "V10928"
## [10929] "V10929" "V10930" "V10931" "V10932" "V10933" "V10934" "V10935" "V10936"
## [10937] "V10937" "V10938" "V10939" "V10940" "V10941" "V10942" "V10943" "V10944"
## [10945] "V10945" "V10946" "V10947" "V10948" "V10949" "V10950" "V10951" "V10952"
## [10953] "V10953" "V10954" "V10955" "V10956" "V10957" "V10958" "V10959" "V10960"
## [10961] "V10961" "V10962" "V10963" "V10964" "V10965" "V10966" "V10967" "V10968"
## [10969] "V10969" "V10970" "V10971" "V10972" "V10973" "V10974" "V10975" "V10976"
## [10977] "V10977" "V10978" "V10979" "V10980" "V10981" "V10982" "V10983" "V10984"
## [10985] "V10985" "V10986" "V10987" "V10988" "V10989" "V10990" "V10991" "V10992"
## [10993] "V10993" "V10994" "V10995" "V10996" "V10997" "V10998" "V10999" "V11000"
## [11001] "V11001" "V11002" "V11003" "V11004" "V11005" "V11006" "V11007" "V11008"
## [11009] "V11009" "V11010" "V11011" "V11012" "V11013" "V11014" "V11015" "V11016"
## [11017] "V11017" "V11018" "V11019" "V11020" "V11021" "V11022" "V11023" "V11024"
## [11025] "V11025" "V11026" "V11027" "V11028" "V11029" "V11030" "V11031" "V11032"
## [11033] "V11033" "V11034" "V11035" "V11036" "V11037" "V11038" "V11039" "V11040"
## [11041] "V11041" "V11042" "V11043" "V11044" "V11045" "V11046" "V11047" "V11048"
## [11049] "V11049" "V11050" "V11051" "V11052" "V11053" "V11054" "V11055" "V11056"
## [11057] "V11057" "V11058" "V11059" "V11060" "V11061" "V11062" "V11063" "V11064"
## [11065] "V11065" "V11066" "V11067" "V11068" "V11069" "V11070" "V11071" "V11072"
## [11073] "V11073" "V11074" "V11075" "V11076" "V11077" "V11078" "V11079" "V11080"
## [11081] "V11081" "V11082" "V11083" "V11084" "V11085" "V11086" "V11087" "V11088"
## [11089] "V11089" "V11090" "V11091" "V11092" "V11093" "V11094" "V11095" "V11096"
## [11097] "V11097" "V11098" "V11099" "V11100" "V11101" "V11102" "V11103" "V11104"
## [11105] "V11105" "V11106" "V11107" "V11108" "V11109" "V11110" "V11111" "V11112"
## [11113] "V11113" "V11114" "V11115" "V11116" "V11117" "V11118" "V11119" "V11120"
## [11121] "V11121" "V11122" "V11123" "V11124" "V11125" "V11126" "V11127" "V11128"
## [11129] "V11129" "V11130" "V11131" "V11132" "V11133" "V11134" "V11135" "V11136"
## [11137] "V11137" "V11138" "V11139" "V11140" "V11141" "V11142" "V11143" "V11144"
## [11145] "V11145" "V11146" "V11147" "V11148" "V11149" "V11150" "V11151" "V11152"
## [11153] "V11153" "V11154" "V11155" "V11156" "V11157" "V11158" "V11159" "V11160"
## [11161] "V11161" "V11162" "V11163" "V11164" "V11165" "V11166" "V11167" "V11168"
## [11169] "V11169" "V11170" "V11171" "V11172" "V11173" "V11174" "V11175" "V11176"
## [11177] "V11177" "V11178" "V11179" "V11180" "V11181" "V11182" "V11183" "V11184"
## [11185] "V11185" "V11186" "V11187" "V11188" "V11189" "V11190" "V11191" "V11192"
## [11193] "V11193" "V11194" "V11195" "V11196" "V11197" "V11198" "V11199" "V11200"
## [11201] "V11201" "V11202" "V11203" "V11204" "V11205" "V11206" "V11207" "V11208"
## [11209] "V11209" "V11210" "V11211" "V11212" "V11213" "V11214" "V11215" "V11216"
## [11217] "V11217" "V11218" "V11219" "V11220" "V11221" "V11222" "V11223" "V11224"
## [11225] "V11225" "V11226" "V11227" "V11228" "V11229" "V11230" "V11231" "V11232"
## [11233] "V11233" "V11234" "V11235" "V11236" "V11237" "V11238" "V11239" "V11240"
## [11241] "V11241" "V11242" "V11243" "V11244" "V11245" "V11246" "V11247" "V11248"
## [11249] "V11249" "V11250" "V11251" "V11252" "V11253" "V11254" "V11255" "V11256"
## [11257] "V11257" "V11258" "V11259" "V11260" "V11261" "V11262" "V11263" "V11264"
## [11265] "V11265" "V11266" "V11267" "V11268" "V11269" "V11270" "V11271" "V11272"
## [11273] "V11273" "V11274" "V11275" "V11276" "V11277" "V11278" "V11279" "V11280"
## [11281] "V11281" "V11282" "V11283" "V11284" "V11285" "V11286" "V11287" "V11288"
## [11289] "V11289" "V11290" "V11291" "V11292" "V11293" "V11294" "V11295" "V11296"
## [11297] "V11297" "V11298" "V11299" "V11300" "V11301" "V11302" "V11303" "V11304"
## [11305] "V11305" "V11306" "V11307" "V11308" "V11309" "V11310" "V11311" "V11312"
## [11313] "V11313" "V11314" "V11315" "V11316" "V11317" "V11318" "V11319" "V11320"
## [11321] "V11321" "V11322" "V11323" "V11324" "V11325" "V11326" "V11327" "V11328"
## [11329] "V11329" "V11330" "V11331" "V11332" "V11333" "V11334" "V11335" "V11336"
## [11337] "V11337" "V11338" "V11339" "V11340" "V11341" "V11342" "V11343" "V11344"
## [11345] "V11345" "V11346" "V11347" "V11348" "V11349" "V11350" "V11351" "V11352"
## [11353] "V11353" "V11354" "V11355" "V11356" "V11357" "V11358" "V11359" "V11360"
## [11361] "V11361" "V11362" "V11363" "V11364" "V11365" "V11366" "V11367" "V11368"
## [11369] "V11369" "V11370" "V11371" "V11372" "V11373" "V11374" "V11375" "V11376"
## [11377] "V11377" "V11378" "V11379" "V11380" "V11381" "V11382" "V11383" "V11384"
## [11385] "V11385" "V11386" "V11387" "V11388" "V11389" "V11390" "V11391" "V11392"
## [11393] "V11393" "V11394" "V11395" "V11396" "V11397" "V11398" "V11399" "V11400"
## [11401] "V11401" "V11402" "V11403" "V11404" "V11405" "V11406" "V11407" "V11408"
## [11409] "V11409" "V11410" "V11411" "V11412" "V11413" "V11414" "V11415" "V11416"
## [11417] "V11417" "V11418" "V11419" "V11420" "V11421" "V11422" "V11423" "V11424"
## [11425] "V11425" "V11426" "V11427" "V11428" "V11429" "V11430" "V11431" "V11432"
## [11433] "V11433" "V11434" "V11435" "V11436" "V11437" "V11438" "V11439" "V11440"
## [11441] "V11441" "V11442" "V11443" "V11444" "V11445" "V11446" "V11447" "V11448"
## [11449] "V11449" "V11450" "V11451" "V11452" "V11453" "V11454" "V11455" "V11456"
## [11457] "V11457" "V11458" "V11459" "V11460" "V11461" "V11462" "V11463" "V11464"
## [11465] "V11465" "V11466" "V11467" "V11468" "V11469" "V11470" "V11471" "V11472"
## [11473] "V11473" "V11474" "V11475" "V11476" "V11477" "V11478" "V11479" "V11480"
## [11481] "V11481" "V11482" "V11483" "V11484" "V11485" "V11486" "V11487" "V11488"
## [11489] "V11489" "V11490" "V11491" "V11492" "V11493" "V11494" "V11495" "V11496"
## [11497] "V11497" "V11498" "V11499" "V11500" "V11501" "V11502" "V11503" "V11504"
## [11505] "V11505" "V11506" "V11507" "V11508" "V11509" "V11510" "V11511" "V11512"
## [11513] "V11513" "V11514" "V11515" "V11516" "V11517" "V11518" "V11519" "V11520"
## [11521] "V11521" "V11522" "V11523" "V11524" "V11525" "V11526" "V11527" "V11528"
## [11529] "V11529" "V11530" "V11531" "V11532" "V11533" "V11534" "V11535" "V11536"
## [11537] "V11537" "V11538" "V11539" "V11540" "V11541" "V11542" "V11543" "V11544"
## [11545] "V11545" "V11546" "V11547" "V11548" "V11549" "V11550" "V11551" "V11552"
## [11553] "V11553" "V11554" "V11555" "V11556" "V11557" "V11558" "V11559" "V11560"
## [11561] "V11561" "V11562" "V11563" "V11564" "V11565" "V11566" "V11567" "V11568"
## [11569] "V11569" "V11570" "V11571" "V11572" "V11573" "V11574" "V11575" "V11576"
## [11577] "V11577" "V11578" "V11579" "V11580" "V11581" "V11582" "V11583" "V11584"
## [11585] "V11585" "V11586" "V11587" "V11588" "V11589" "V11590" "V11591" "V11592"
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## [11977] "V11977" "V11978" "V11979" "V11980" "V11981" "V11982" "V11983" "V11984"
## [11985] "V11985" "V11986" "V11987" "V11988" "V11989" "V11990" "V11991" "V11992"
## [11993] "V11993" "V11994" "V11995" "V11996" "V11997" "V11998" "V11999" "V12000"
##
## $class
## [1] "data.frame"
##
## $row.names
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200npuntos <- nrow(imagenductilfragilfatigaxeydf)
base_bspline <- create.bspline.basis(c(1, npuntos), 150)
plot(base_bspline)
summary(base_bspline)##
## Basis object:
##
## Type: bspline
##
## Range: 1 to 200
##
## Number of basis functions: 150# Selección de lambda
gcv <- NULL
lambda_list <- seq(-2, 4, length.out = 30)
# estos son las potencias de 10 de variación del lambda
# inicia con valores muy cercanos a cero 10^-2 hasta 10^4
matrix_data <- as.matrix(imagenductilfragilfatigaxeydf)
for (lambda in lambda_list) {
lambda <- 10**lambda
param_lambda <- fdPar(base_bspline,
2,
lambda)
fd_smoothductilfragilfatiga <- smooth.basis(argvals = seq(1,
nrow(matrix_data)),
y = matrix_data,
fdParobj = param_lambda)
gcv <- c(gcv, sum(fd_smoothductilfragilfatiga$gcv))
}
# Se esta tomando como criterio total la suma de las VC de todas las curvas
plotgcv <-
data.frame(log_lambda = lambda_list,
gcv = gcv) %>% ggplot(aes(x = log_lambda, y = gcv)) +
geom_line(color = "purple", linetype = "dashed") +
theme_light() +
scale_x_continuous(limits = c(0, 4))
plotly::ggplotly(plotgcv)selected_lamdba <- 10**lambda_list[which.min(gcv)]
print(selected_lamdba)## [1] 0.02592944print(log(selected_lamdba, 10))## [1] -1.586207# entrega el valor de lambda donde se minimiza la suma de VC y log de lambda
# Suavizamiento con el lambda elegido
param_lambda <- fdPar(base_bspline,
2,
lambda = selected_lamdba)
fd_smoothductilfragilfatiga <- smooth.basis(argvals = seq(1,
nrow(matrix_data)),
y = matrix_data,
fdParobj = param_lambda)
plot(fd_smoothductilfragilfatiga, xlab= "Pixel", ylab = "Intensidad")
## [1] "done"plot(fd_smoothductilfragilfatiga$fd[1], xlab= "Pixel", ylab = "Intensidad")
## [1] "done"# una curva
# Comparación de curvas originales y suavizadas
fd_fittedductilfragilfatiga <- fitted(fd_smoothductilfragilfatiga)
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,1],col="2")
4.2 Estimación de PCA y scores de todas las graficas en conjunto con 150 harmonicos (aca variar el numero de armónicos)
Los porcentajes de varianza que recogen las diez primeras funciones principales son: 1.620517e+01 3.280065e+00 2.282422e+00 2.036705e+00 1.953267e+00 1.792524e+00 1.645079e+00 1.640718e+00 1.630027e+00 1.617558e+00. Hasta la funcion propia 82 se acumula el 90.10906% de la varianza.
# Estimación de valores propios, scores y funciones propias para las curvas combinadas de dúctil, frágil y fatiga
Curvas_Ductil_Fragil_Fatiga.pca<-pca.fd(fd_smoothductilfragilfatiga$fd,nharm=150,harmfdPar = fdPar(fd_smoothductilfragilfatiga$fd))
# Escoger armonicos en funcion del porcentaje de varianza
lambda_comb<-Curvas_Ductil_Fragil_Fatiga.pca$values
# Valores propios
v_comb<-Curvas_Ductil_Fragil_Fatiga.pca$harmonics
# funciones propias
plot(v_comb[1:3])
## [1] "done"# Visualización primeras tres funciones principales
scores_comb<-Curvas_Ductil_Fragil_Fatiga.pca$scores
# scores
sum_val_prop <- sum(Curvas_Ductil_Fragil_Fatiga.pca$values)
# suma de valores propios
Val_prop_percent <- (Curvas_Ductil_Fragil_Fatiga.pca$values/sum_val_prop)*100;Val_prop_percent## [1] 1.620644e+01 3.280059e+00 2.282260e+00 2.036683e+00 1.953137e+00
## [6] 1.792502e+00 1.644910e+00 1.640767e+00 1.630118e+00 1.617428e+00
## [11] 1.489976e+00 1.438652e+00 1.410583e+00 1.364453e+00 1.337212e+00
## [16] 1.315606e+00 1.265654e+00 1.236721e+00 1.202021e+00 1.170329e+00
## [21] 1.146386e+00 1.105780e+00 1.089872e+00 1.075171e+00 1.051185e+00
## [26] 1.048959e+00 1.013120e+00 9.665306e-01 9.644357e-01 9.626638e-01
## [31] 9.294053e-01 9.125078e-01 9.044202e-01 8.908965e-01 8.799031e-01
## [36] 8.590860e-01 8.329008e-01 8.220929e-01 8.033277e-01 7.887951e-01
## [41] 7.808222e-01 7.642014e-01 7.599564e-01 7.554088e-01 7.445708e-01
## [46] 7.255069e-01 7.125825e-01 6.917065e-01 6.805968e-01 6.714922e-01
## [51] 6.673596e-01 6.583499e-01 6.503759e-01 6.324194e-01 6.269833e-01
## [56] 6.095032e-01 6.029750e-01 5.903988e-01 5.887136e-01 5.744889e-01
## [61] 5.679565e-01 5.635257e-01 5.581181e-01 5.339415e-01 5.230769e-01
## [66] 5.064254e-01 5.038554e-01 4.938183e-01 4.907079e-01 4.817517e-01
## [71] 4.682597e-01 4.556852e-01 4.505787e-01 4.468858e-01 4.329680e-01
## [76] 4.292581e-01 4.162757e-01 3.992664e-01 3.932808e-01 3.841740e-01
## [81] 3.803664e-01 3.772694e-01 3.619537e-01 3.541131e-01 3.456969e-01
## [86] 3.410059e-01 3.266573e-01 3.228928e-01 3.161324e-01 3.049117e-01
## [91] 2.916372e-01 2.895922e-01 2.846308e-01 2.754148e-01 2.694354e-01
## [96] 2.581949e-01 2.511557e-01 2.420246e-01 2.369330e-01 2.299882e-01
## [101] 2.235730e-01 2.134765e-01 2.074576e-01 2.021717e-01 1.938217e-01
## [106] 1.818580e-01 1.784887e-01 1.711137e-01 1.652111e-01 1.605650e-01
## [111] 1.469526e-01 1.395333e-01 1.384630e-01 1.329312e-01 1.263923e-01
## [116] 1.219247e-01 1.182595e-01 1.098905e-01 1.078044e-01 1.015643e-01
## [121] 9.583792e-02 9.225233e-02 8.899882e-02 8.293576e-02 7.754296e-02
## [126] 7.507881e-02 7.105198e-02 6.703905e-02 6.383861e-02 5.857575e-02
## [131] 5.469912e-02 5.257265e-02 4.994933e-02 4.613382e-02 4.363242e-02
## [136] 4.171746e-02 3.953864e-02 3.767121e-02 3.731400e-02 3.399513e-02
## [141] 3.290323e-02 3.184443e-02 3.111074e-02 3.017683e-02 2.888971e-02
## [146] 2.786749e-02 2.646151e-02 2.614341e-02 8.407452e-04 7.602429e-04# Porcentaje de la varianza de valores propios4.3 Paso de las curvas a su representación en componentes principales con 150 harmonicos
Curvas_Ductil_Fragil_Fatiga.EnBasePCA <- reconsCurves(imagenductilfragilfatigaxey,Curvas_Ductil_Fragil_Fatiga.pca)
plot(Curvas_Ductil_Fragil_Fatiga.EnBasePCA, xlab= "Pixel", ylab = "Intensidad")
## [1] "done"plot(Curvas_Ductil_Fragil_Fatiga.EnBasePCA[1], xlab= "Pixel", ylab = "Intensidad")
## [1] "done"# una curva
# Comparación de curvas originales y suavizadas
x1 <- seq(1:200)
fd_fittedductilfragilfatiga <- eval.fd(x1,Curvas_Ductil_Fragil_Fatiga.EnBasePCA)
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,1],col="2")
plot(imagenductilfragilfatigaxey[,6000],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,6000],col="2")
plot(imagenductilfragilfatigaxey[,12000],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,12000],col="2")
4.4. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (familia binomial)
Corre en 2 minutos.
La fracción de acierto global es de 0.585, para imágenes dúctiles es de 0.818, para imágenes frágiles de 0.698 y para imágenes de fatiga 0.238.
Por fotos:
La fracción de acierto global es de 0.777.
Tres de tres dúctiles se adjudican a dúctil. Acierto 100%. Tres de tres frágiles se adjudican correctamente. Acierto 100%. Uno de tres de fatiga se adjudicó correcta. Acierto 33%.
(Dio igual que con base bspline cuando se usan todos los 150 harmonicos).
fd_fittedductilfragilfatiga_t <- t(fd_fittedductilfragilfatiga)
Curvas_Ent_PCA_Matriz <- rbind(fd_fittedductilfragilfatiga_t[1:2800,],fd_fittedductilfragilfatiga_t[4001:6800,],fd_fittedductilfragilfatiga_t[8001:10800,])
Curvas_Ens_PCA_Matriz <- rbind(fd_fittedductilfragilfatiga_t[2801:4000,],fd_fittedductilfragilfatiga_t[6801:8000,],fd_fittedductilfragilfatiga_t[10801:12000,])
# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frágil y categoría 3 es fatiga
CatEnt <- as.factor(c(rep(1,2800), rep(2,2800), rep(3,2800)))
# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frágil y categoría 3 es fatiga
CatEns <- as.factor(c(rep(1,1200), rep(2,1200), rep(3,1200)))
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntfdata <- fdata(Curvas_Ent_PCA_Matriz,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = binomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
fd_smoothEnsfdata <- fdata(Curvas_Ens_PCA_Matriz,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 982 44 174
## 2 132 836 232
## 3 183 730 287t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.5847222t[1,1]/1200## [1] 0.8183333t[2,2]/1200## [1] 0.6966667t[3,3]/1200## [1] 0.2391667Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 394 0 6
## [2,] 299 17 84
## [3,] 289 27 84# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 60 236 104
## [2,] 2 395 3
## [3,] 70 205 125# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 47 249 104
## [2,] 129 134 137
## [3,] 7 347 46# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenductilfragilfatigaxey[,2801],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
4.5 Estimación de PCA y scores de todas las graficas en conjunto con 1 harmonicos (aca variar el numero de armónicos)
Los porcentajes de varianza que recogen las diez primeras funciones principales son: 1.620517e+01 3.280065e+00 2.282422e+00 2.036705e+00 1.953267e+00 1.792524e+00 1.645079e+00 1.640718e+00 1.630027e+00 1.617558e+00. Hasta la funcion propia 82 se acumula el 90.10906% de la varianza.
# Estimación de valores propios, scores y funciones propias para las curvas combinadas de dúctil, frágil y fatiga
Curvas_Ductil_Fragil_Fatiga.pca<-pca.fd(fd_smoothductilfragilfatiga$fd,nharm=1,harmfdPar = fdPar(fd_smoothductilfragilfatiga$fd))
# Escoger armonicos en funcion del porcentaje de varianza
lambda_comb<-Curvas_Ductil_Fragil_Fatiga.pca$values
# Valores propios
v_comb<-Curvas_Ductil_Fragil_Fatiga.pca$harmonics
# funciones propias
plot(v_comb[1:3])
## [1] "done"# Visualización primeras tres funciones principales
scores_comb<-Curvas_Ductil_Fragil_Fatiga.pca$scores
# scores
sum_val_prop <- sum(Curvas_Ductil_Fragil_Fatiga.pca$values)
# suma de valores propios
Val_prop_percent <- (Curvas_Ductil_Fragil_Fatiga.pca$values/sum_val_prop)*100;Val_prop_percent## [1] 1.620644e+01 3.280059e+00 2.282260e+00 2.036683e+00 1.953137e+00
## [6] 1.792502e+00 1.644910e+00 1.640767e+00 1.630118e+00 1.617428e+00
## [11] 1.489976e+00 1.438652e+00 1.410583e+00 1.364453e+00 1.337212e+00
## [16] 1.315606e+00 1.265654e+00 1.236721e+00 1.202021e+00 1.170329e+00
## [21] 1.146386e+00 1.105780e+00 1.089872e+00 1.075171e+00 1.051185e+00
## [26] 1.048959e+00 1.013120e+00 9.665306e-01 9.644357e-01 9.626638e-01
## [31] 9.294053e-01 9.125078e-01 9.044202e-01 8.908965e-01 8.799031e-01
## [36] 8.590860e-01 8.329008e-01 8.220929e-01 8.033277e-01 7.887951e-01
## [41] 7.808222e-01 7.642014e-01 7.599564e-01 7.554088e-01 7.445708e-01
## [46] 7.255069e-01 7.125825e-01 6.917065e-01 6.805968e-01 6.714922e-01
## [51] 6.673596e-01 6.583499e-01 6.503759e-01 6.324194e-01 6.269833e-01
## [56] 6.095032e-01 6.029750e-01 5.903988e-01 5.887136e-01 5.744889e-01
## [61] 5.679565e-01 5.635257e-01 5.581181e-01 5.339415e-01 5.230769e-01
## [66] 5.064254e-01 5.038554e-01 4.938183e-01 4.907079e-01 4.817517e-01
## [71] 4.682597e-01 4.556852e-01 4.505787e-01 4.468858e-01 4.329680e-01
## [76] 4.292581e-01 4.162757e-01 3.992664e-01 3.932808e-01 3.841740e-01
## [81] 3.803664e-01 3.772694e-01 3.619537e-01 3.541131e-01 3.456969e-01
## [86] 3.410059e-01 3.266573e-01 3.228928e-01 3.161324e-01 3.049117e-01
## [91] 2.916372e-01 2.895922e-01 2.846308e-01 2.754148e-01 2.694354e-01
## [96] 2.581949e-01 2.511557e-01 2.420246e-01 2.369330e-01 2.299882e-01
## [101] 2.235730e-01 2.134765e-01 2.074576e-01 2.021717e-01 1.938217e-01
## [106] 1.818580e-01 1.784887e-01 1.711137e-01 1.652111e-01 1.605650e-01
## [111] 1.469526e-01 1.395333e-01 1.384630e-01 1.329312e-01 1.263923e-01
## [116] 1.219247e-01 1.182595e-01 1.098905e-01 1.078044e-01 1.015643e-01
## [121] 9.583792e-02 9.225233e-02 8.899882e-02 8.293576e-02 7.754296e-02
## [126] 7.507881e-02 7.105198e-02 6.703905e-02 6.383861e-02 5.857575e-02
## [131] 5.469912e-02 5.257265e-02 4.994933e-02 4.613382e-02 4.363242e-02
## [136] 4.171746e-02 3.953864e-02 3.767121e-02 3.731400e-02 3.399513e-02
## [141] 3.290323e-02 3.184443e-02 3.111074e-02 3.017683e-02 2.888971e-02
## [146] 2.786749e-02 2.646151e-02 2.614341e-02 8.407452e-04 7.602429e-04# Porcentaje de la varianza de valores propios4.6 Paso de las curvas a su representación en componentes principales con 1 harmonicos
Curvas_Ductil_Fragil_Fatiga.EnBasePCA <- reconsCurves(imagenductilfragilfatigaxey,Curvas_Ductil_Fragil_Fatiga.pca)
plot(Curvas_Ductil_Fragil_Fatiga.EnBasePCA, xlab= "Pixel", ylab = "Intensidad")
## [1] "done"plot(Curvas_Ductil_Fragil_Fatiga.EnBasePCA[1], xlab= "Pixel", ylab = "Intensidad")
## [1] "done"# una curva
# Comparación de curvas originales y suavizadas
x1 <- seq(1:200)
fd_fittedductilfragilfatiga <- eval.fd(x1,Curvas_Ductil_Fragil_Fatiga.EnBasePCA)
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,1],col="2")
plot(imagenductilfragilfatigaxey[,6000],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,6000],col="2")
plot(imagenductilfragilfatigaxey[,12000],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,12000],col="2")
4.7. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (familia binomial)
Corre en 2 minutos.
La fracción de acierto global es de 0.6066667, para imágenes dúctiles es de 0.8741667, para imágenes frágiles de 0.705 y para imágenes de fatiga 0.2408333.
Por fotos:
La fracción de acierto global es de 0.777.
Tres de tres dúctiles se adjudican a dúctil. Acierto 100%. Tres de tres frágiles se adjudican correctamente. Acierto 100%. Uno de tres de fatiga se adjudicó correcta. Acierto 33%.
fd_fittedductilfragilfatiga_t <- t(fd_fittedductilfragilfatiga)
Curvas_Ent_PCA_Matriz <- rbind(fd_fittedductilfragilfatiga_t[1:2800,],fd_fittedductilfragilfatiga_t[4001:6800,],fd_fittedductilfragilfatiga_t[8001:10800,])
Curvas_Ens_PCA_Matriz <- rbind(fd_fittedductilfragilfatiga_t[2801:4000,],fd_fittedductilfragilfatiga_t[6801:8000,],fd_fittedductilfragilfatiga_t[10801:12000,])
# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frágil y categoría 3 es fatiga
CatEnt <- as.factor(c(rep(1,2800), rep(2,2800), rep(3,2800)))
# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frágil y categoría 3 es fatiga
CatEns <- as.factor(c(rep(1,1200), rep(2,1200), rep(3,1200)))
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntfdata <- fdata(Curvas_Ent_PCA_Matriz,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = binomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
fd_smoothEnsfdata <- fdata(Curvas_Ens_PCA_Matriz,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 1049 32 119
## 2 131 846 223
## 3 177 734 289t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.6066667t[1,1]/1200## [1] 0.8741667t[2,2]/1200## [1] 0.705t[3,3]/1200## [1] 0.2408333Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 398 0 2
## [2,] 326 11 63
## [3,] 325 21 54# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 60 239 101
## [2,] 0 395 5
## [3,] 71 212 117# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 59 275 66
## [2,] 115 106 179
## [3,] 3 353 44# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenductilfragilfatigaxey[,2801],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
4.8 Estimación de PCA y scores de todas las graficas en conjunto con 2 harmonicos (aca variar el numero de armónicos)
Los porcentajes de varianza que recogen las diez primeras funciones principales son: 1.620517e+01 3.280065e+00 2.282422e+00 2.036705e+00 1.953267e+00 1.792524e+00 1.645079e+00 1.640718e+00 1.630027e+00 1.617558e+00. Hasta la funcion propia 82 se acumula el 90.10906% de la varianza.
# Estimación de valores propios, scores y funciones propias para las curvas combinadas de dúctil, frágil y fatiga
Curvas_Ductil_Fragil_Fatiga.pca<-pca.fd(fd_smoothductilfragilfatiga$fd,nharm=2,harmfdPar = fdPar(fd_smoothductilfragilfatiga$fd))
# Escoger armonicos en funcion del porcentaje de varianza
lambda_comb<-Curvas_Ductil_Fragil_Fatiga.pca$values
# Valores propios
v_comb<-Curvas_Ductil_Fragil_Fatiga.pca$harmonics
# funciones propias
plot(v_comb[1:2])
## [1] "done"# Visualización primeras tres funciones principales
scores_comb<-Curvas_Ductil_Fragil_Fatiga.pca$scores
# scores
sum_val_prop <- sum(Curvas_Ductil_Fragil_Fatiga.pca$values)
# suma de valores propios
Val_prop_percent <- (Curvas_Ductil_Fragil_Fatiga.pca$values/sum_val_prop)*100;Val_prop_percent## [1] 1.620644e+01 3.280059e+00 2.282260e+00 2.036683e+00 1.953137e+00
## [6] 1.792502e+00 1.644910e+00 1.640767e+00 1.630118e+00 1.617428e+00
## [11] 1.489976e+00 1.438652e+00 1.410583e+00 1.364453e+00 1.337212e+00
## [16] 1.315606e+00 1.265654e+00 1.236721e+00 1.202021e+00 1.170329e+00
## [21] 1.146386e+00 1.105780e+00 1.089872e+00 1.075171e+00 1.051185e+00
## [26] 1.048959e+00 1.013120e+00 9.665306e-01 9.644357e-01 9.626638e-01
## [31] 9.294053e-01 9.125078e-01 9.044202e-01 8.908965e-01 8.799031e-01
## [36] 8.590860e-01 8.329008e-01 8.220929e-01 8.033277e-01 7.887951e-01
## [41] 7.808222e-01 7.642014e-01 7.599564e-01 7.554088e-01 7.445708e-01
## [46] 7.255069e-01 7.125825e-01 6.917065e-01 6.805968e-01 6.714922e-01
## [51] 6.673596e-01 6.583499e-01 6.503759e-01 6.324194e-01 6.269833e-01
## [56] 6.095032e-01 6.029750e-01 5.903988e-01 5.887136e-01 5.744889e-01
## [61] 5.679565e-01 5.635257e-01 5.581181e-01 5.339415e-01 5.230769e-01
## [66] 5.064254e-01 5.038554e-01 4.938183e-01 4.907079e-01 4.817517e-01
## [71] 4.682597e-01 4.556852e-01 4.505787e-01 4.468858e-01 4.329680e-01
## [76] 4.292581e-01 4.162757e-01 3.992664e-01 3.932808e-01 3.841740e-01
## [81] 3.803664e-01 3.772694e-01 3.619537e-01 3.541131e-01 3.456969e-01
## [86] 3.410059e-01 3.266573e-01 3.228928e-01 3.161324e-01 3.049117e-01
## [91] 2.916372e-01 2.895922e-01 2.846308e-01 2.754148e-01 2.694354e-01
## [96] 2.581949e-01 2.511557e-01 2.420246e-01 2.369330e-01 2.299882e-01
## [101] 2.235730e-01 2.134765e-01 2.074576e-01 2.021717e-01 1.938217e-01
## [106] 1.818580e-01 1.784887e-01 1.711137e-01 1.652111e-01 1.605650e-01
## [111] 1.469526e-01 1.395333e-01 1.384630e-01 1.329312e-01 1.263923e-01
## [116] 1.219247e-01 1.182595e-01 1.098905e-01 1.078044e-01 1.015643e-01
## [121] 9.583792e-02 9.225233e-02 8.899882e-02 8.293576e-02 7.754296e-02
## [126] 7.507881e-02 7.105198e-02 6.703905e-02 6.383861e-02 5.857575e-02
## [131] 5.469912e-02 5.257265e-02 4.994933e-02 4.613382e-02 4.363242e-02
## [136] 4.171746e-02 3.953864e-02 3.767121e-02 3.731400e-02 3.399513e-02
## [141] 3.290323e-02 3.184443e-02 3.111074e-02 3.017683e-02 2.888971e-02
## [146] 2.786749e-02 2.646151e-02 2.614341e-02 8.407452e-04 7.602429e-04# Porcentaje de la varianza de valores propios4.9 Paso de las curvas a su representación en componentes principales con 2 harmonicos
Curvas_Ductil_Fragil_Fatiga.EnBasePCA <- reconsCurves(imagenductilfragilfatigaxey,Curvas_Ductil_Fragil_Fatiga.pca)
plot(Curvas_Ductil_Fragil_Fatiga.EnBasePCA, xlab= "Pixel", ylab = "Intensidad")
## [1] "done"plot(Curvas_Ductil_Fragil_Fatiga.EnBasePCA[1], xlab= "Pixel", ylab = "Intensidad")
## [1] "done"# una curva
# Comparación de curvas originales y suavizadas
x1 <- seq(1:200)
fd_fittedductilfragilfatiga <- eval.fd(x1,Curvas_Ductil_Fragil_Fatiga.EnBasePCA)
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,1],col="2")
plot(imagenductilfragilfatigaxey[,6000],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,6000],col="2")
plot(imagenductilfragilfatigaxey[,12000],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,12000],col="2")
4.10. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (familia binomial)
Corre en 2 minutos.
La fracción de acierto global es de 0.6047222, para imágenes dúctiles es de 0.8608333, para imágenes frágiles de 0.7091667 y para imágenes de fatiga 0.2441667.
Por fotos:
La fracción de acierto global es de 0.777.
Tres de tres dúctiles se adjudican a dúctil. Acierto 100%. Tres de tres frágiles se adjudican correctamente. Acierto 100%. Uno de tres de fatiga se adjudicó correcta. Acierto 33%.
fd_fittedductilfragilfatiga_t <- t(fd_fittedductilfragilfatiga)
Curvas_Ent_PCA_Matriz <- rbind(fd_fittedductilfragilfatiga_t[1:2800,],fd_fittedductilfragilfatiga_t[4001:6800,],fd_fittedductilfragilfatiga_t[8001:10800,])
Curvas_Ens_PCA_Matriz <- rbind(fd_fittedductilfragilfatiga_t[2801:4000,],fd_fittedductilfragilfatiga_t[6801:8000,],fd_fittedductilfragilfatiga_t[10801:12000,])
# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frágil y categoría 3 es fatiga
CatEnt <- as.factor(c(rep(1,2800), rep(2,2800), rep(3,2800)))
# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frágil y categoría 3 es fatiga
CatEns <- as.factor(c(rep(1,1200), rep(2,1200), rep(3,1200)))
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntfdata <- fdata(Curvas_Ent_PCA_Matriz,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = binomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
fd_smoothEnsfdata <- fdata(Curvas_Ens_PCA_Matriz,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 1033 32 135
## 2 124 851 225
## 3 169 738 293t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.6047222t[1,1]/1200## [1] 0.8608333t[2,2]/1200## [1] 0.7091667t[3,3]/1200## [1] 0.2441667Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 399 0 1
## [2,] 332 11 57
## [3,] 302 21 77# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 59 240 101
## [2,] 0 395 5
## [3,] 65 216 119# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 63 275 62
## [2,] 101 111 188
## [3,] 5 352 43# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenductilfragilfatigaxey[,2801],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")